WO2023103377A1 - 标定方法及装置、电子设备、存储介质及计算机程序产品 - Google Patents
标定方法及装置、电子设备、存储介质及计算机程序产品 Download PDFInfo
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- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
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- the embodiment of the present disclosure is based on the Chinese patent application with the application number 202111497801.X, the application date is December 09, 2021, and the application name is "calibration method and device, electronic equipment and storage medium", and requires the Chinese patent application Priority, the entire content of the Chinese patent application is hereby incorporated by reference into this disclosure.
- the present disclosure relates to but not limited to the technical field of computer vision, and in particular relates to a calibration method and device, electronic equipment, storage media and computer program products.
- the monitoring system is one of the most widely used systems in the security system. According to the camera parameters of the monitoring camera in the monitoring system, a variety of information can be obtained from the monitored scene, such as the specific position, height, and walking speed of pedestrians. However, the large number and wide distribution of surveillance cameras in cities make it difficult for surveillance cameras to obtain camera parameters. Therefore, there is an urgent need for a low-cost camera calibration method.
- Embodiments of the present disclosure provide a calibration method and device, electronic equipment, a storage medium, and a computer program product.
- An embodiment of the present disclosure provides a calibration method, including: detecting an image to be processed, and acquiring a target object in the image to be processed; determining preset feature points of the target object; and determining according to the preset feature points A mapping matrix corresponding to the preset feature points; according to the mapping matrix and the preset feature points, parameter information of an image acquisition device is obtained, and the image to be processed is acquired by the image acquisition device.
- the mapping matrix can be determined through the preset feature points in the area where any target object is located in the image, and then the internal reference information and pose information of the image acquisition device can be determined, without the need for the same target object to appear in multiple
- the preset position can complete the self-calibration process without the cooperation of the target object, which reduces the manual workload and calibration cost, and is suitable for scenes with a large number of image acquisition devices and wide distribution. For example, it is suitable for many urban surveillance systems. Camera self-calibration.
- An embodiment of the present disclosure also provides a calibration device, including: a target object acquisition part configured to detect an image to be processed, and acquire a target object in the image to be processed; a feature point determination part configured to determine the The preset feature points of the target object; the mapping matrix determining part is configured to determine a mapping matrix corresponding to the preset feature points according to the preset feature points; the parameter information determining part is configured to determine according to the mapping matrix As well as the preset feature points, parameter information of an image acquisition device is obtained, and the image to be processed is acquired by the image acquisition device.
- An embodiment of the present disclosure also provides an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to invoke the instructions stored in the memory to execute the above method.
- An embodiment of the present disclosure also provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the foregoing method is implemented.
- An embodiment of the present disclosure also provides a computer program product, where the computer program product includes a computer program or an instruction, and when the computer program or instruction is run on an electronic device, the electronic device is made to execute the above method.
- FIG. 1 is a schematic flowchart of a calibration method provided by an embodiment of the present disclosure
- FIG. 2A is a schematic diagram of key points of an object in an image to be processed provided by an embodiment of the present disclosure
- FIG. 2B is a schematic diagram of key points of an object in an image to be processed provided by an embodiment of the present disclosure
- FIG. 3 is a schematic diagram of key points of an object in an image to be processed provided by an embodiment of the present disclosure
- FIG. 4A is a schematic diagram of a mask image of a target object in an image to be processed provided by an embodiment of the present disclosure
- FIG. 4B is a schematic diagram of a mask image of a target object in an image to be processed provided by an embodiment of the present disclosure
- FIG. 5 is a schematic diagram of the application of the calibration method provided by the embodiment of the present disclosure.
- FIG. 6 is a structural block diagram of a calibration device provided by an embodiment of the present disclosure.
- FIG. 7 is a block diagram of an electronic device provided by an embodiment of the present disclosure.
- Fig. 8 is a block diagram of an electronic device provided by an embodiment of the present disclosure.
- the calibration of the monitoring camera includes the following two methods:
- the first one is the calibration of the calibration board.
- the calibration of the calibration board needs to manually place the calibration board in the field of view of the surveillance camera, and obtain the camera video stream at the corresponding time.
- this method is cumbersome and time-consuming, and requires huge labor costs, so it is not applicable in city-level scenarios;
- the second is camera self-calibration.
- Camera self-calibration is to calculate the camera parameters by detecting the positions of the heads and feet of multiple people in different positions of the picture in the video, and estimating the vanishing point of the camera through geometric methods.
- the camera self-calibration method requires the same person to capture at different positions in the picture, and the robustness of the camera parameter estimation results is low. For example, if the image acquisition device moves, the calibrated parameters may become invalid and need to be re-calibrated.
- Fig. 1 is a schematic flowchart of a calibration method proposed by an embodiment of the present disclosure. As shown in Fig. 1, the method includes steps S11 to S14, wherein:
- Step S11 detecting the image to be processed, and acquiring the target object in the image to be processed
- Step S12 determining preset feature points of the target object
- Step S13 determining a mapping matrix corresponding to the preset feature points according to the preset feature points
- Step S14 according to the mapping matrix and the preset feature points, obtain parameter information of an image acquisition device, and the image to be processed is acquired by the image acquisition device.
- the mapping matrix is determined through the preset feature points of the area where any target object is located in the image to be processed, and then the parameter information of the image acquisition device is determined according to the mapping matrix and the preset feature points. Therefore, the calibration method proposed by the embodiments of the present disclosure can complete the self-calibration process without the same target object appearing in multiple preset positions in the field of view of the image acquisition device, and without the cooperation of the target object, thereby reducing the manual workload and calibration. Cost, suitable for scenes with a large number of image acquisition devices and wide distribution, for example, it is suitable for self-calibration of many cameras in urban surveillance systems.
- the calibration is performed based on the preset feature points of the target object in the image to be processed acquired by the image acquisition device, wherein the preset feature points may be pixels in the area where the target object is located that can represent the size of the target object
- the pixel points (or feature points) may be, for example, the pixel points on the top of the head and the bottom of the feet of the target object, or the pixel points on both ends of the shoulders of the target object, and the like.
- the present disclosure does not limit which part of the target object the preset feature point can be.
- the preset feature points of the target object are obtained, according to the relationship between the preset feature points (for example, the distance between the top of the head and the bottom of the target object (ie, the height of the target object) is fixed, so , there is a specific distance relationship between two points) to obtain a mapping matrix, and then based on the mapping matrix to solve the internal parameter information and pose information of the image acquisition device, that is, to automatically calibrate the image acquisition device.
- the relationship between the preset feature points for example, the distance between the top of the head and the bottom of the target object (ie, the height of the target object) is fixed, so , there is a specific distance relationship between two points
- the image acquisition device can be set at any position to capture video within the field of view of the image acquisition device.
- the image acquisition device can be set at any position to capture video within the field of view of the image acquisition device.
- the self-calibration needs to determine the relationship between the preset feature points of the target object, for example, the distance relationship between the top of the head and the bottom of the target object
- a mapping matrix is obtained based on the relationship for calibration. Therefore, it is necessary to determine a qualified object among multiple objects in the image to be processed as the target object, that is, the preset feature points of the target object can be determined based on the pixel points of the area where the target object is located, and the preset feature points of the target object can be determined based on the preset feature points to determine the above mapping matrix.
- the mapping matrix is determined based on the distance between the top of the head and the soles of the feet of the target object, it can be screened out among multiple objects that both the top of the head and the soles of the feet can be detected (not blocked), and An object whose posture is a standing posture (that is, the distance relationship between the top of the head and the sole of the feet conforms to the preset fixed distance relationship, and the gap between the distance and the preset fixed distance will not be too large due to the posture of the target object) as target.
- mapping matrix is determined based on the distance between the shoulders of the target object
- multiple objects can be screened out to face or face away from the image acquisition device (that is, the distance relationship between the shoulders An object that conforms to the preset fixed distance relationship and does not cause too large a gap between the distance and the preset fixed distance due to the angle of the target object) and whose shoulders are not occluded is used as the target object.
- the target object is used as the target object.
- step S11 when performing target object detection, the detection may be based on the above factors, that is, this step S11 may include step S111 and step S112, wherein:
- Step S111 detecting multiple objects in the image to be processed
- Step S112 according to at least one of the poses of the multiple objects and the occlusion states of the multiple objects, filter the multiple objects to obtain the target object, that is, determine how many objects in the image to be processed At least one of the pose and occlusion state of an object, and filter based on at least one of the pose and occlusion state, for example, filter out the standing posture, and the unoccluded object is used as the target object, or filter out the directly opposite object. Or the object facing away from the image acquisition device and not being occluded is used as the target object.
- step S112 when performing screening, key points of each object may be detected, and at least one of the aforementioned pose and occlusion state may be obtained based on the key points, so as to screen multiple objects. Therefore, step S112 includes step S1121 to step S1123, wherein:
- Step S1121 acquiring key points of the plurality of objects
- Step S1122 according to the key points of each of the objects, determine the pose of each of the objects
- Step S1123 according to the posture of each of the objects, the multiple objects are screened to obtain the target object.
- the key points of each object may be key points representing the body structure of the target object, for example, the key points may include head key points, shoulder key points, elbow key points, hand key points , waist key points, knee key points, foot key points, etc.
- the present disclosure does not limit the type and position of key points of each object.
- step S1121 may include step S1121A and step S1121B, wherein:
- Step S1121A obtaining the location information of each of the objects
- Step S1121B according to the location information of each of the objects, the key points of the multiple objects are obtained.
- the image to be processed may be detected through a deep learning neural network to obtain position information of multiple objects, and the disclosure does not limit the detection method.
- the position information may represent the position of each object.
- the position information may be a detection frame for frame-selecting the object, a contour line for delineating the outline of the object, or coordinate information representing key points of the target object, etc. .
- the present disclosure does not limit the specific form of the location information.
- the key points of each object can be obtained according to the position information of each object.
- the position information is a detection frame for selecting objects
- the The image blocks in each detection frame are subjected to key point detection to obtain the key points of each object.
- the key point detection is only performed on the image blocks in the detection frame without the need for full image detection, which can reduce the calculation amount of key point detection processing.
- the position information is a contour line depicting the contour of the object, only the area inside the contour line can be detected to obtain the key points of each object, which can also reduce the calculation amount of key point detection.
- keypoint detection may be performed by a deep learning neural network. The present disclosure does not limit the specific method of key point detection.
- target objects meeting the conditions may be filtered out based on the key points. For example, when the mapping matrix is determined by the distance between the top of the head and the soles of the feet, target objects whose tops of the head and soles of the feet can be detected (that is, not occluded) and whose posture is standing can be filtered out. For another example, when the mapping matrix is determined by the distance between the shoulders, target objects whose both shoulders can be detected (that is, are not blocked) and are facing or facing away from the image acquisition device can be screened out.
- the pose of each object can be determined by the angle of the line connecting the keypoints. For example, when the angle between the upper body key point line and the thigh key point line is large, it can be considered that the object's posture is not standing. For example, it can be judged that the object's posture is sitting or bowing. For another example, it is possible to judge the connection line between the shoulder key point and the waist key point, and the angle of the connection line between the waist key point and the knee key point. If the angle is large (for example, greater than or equal to 30°, etc. Angle threshold), then the posture of the object can be considered as a non-standing posture.
- the angle is large (for example, greater than or equal to 30°, etc. Angle threshold)
- FIG. 2A and FIG. 2B are schematic diagrams of key points of an object in an image to be processed proposed by an embodiment of the present disclosure.
- the angle between the line 21 between the shoulder key point and the waist key point of the target object and the line 22 between the waist key point and the knee key point is relatively small, for example, less than 30°, Then the posture of the object can be considered as a standing posture.
- the angle between the line 23 between the shoulder key point and the waist key point of the target object and the line 24 between the waist key point and the knee key point is relatively large, for example, greater than 30°, Then it can be considered that the posture of the object is a non-standing posture.
- multiple objects may be screened based on the poses of the objects determined in the above manner, and objects whose poses are not standing poses may be excluded.
- the posture of the object can also be determined in other ways.
- it can be determined based on the direction of travel of the target object, etc. Screening, for example, if the direction of travel of the target object is parallel to the height direction (for example, the Y-axis direction) of the image to be processed, then the target object is facing directly or facing away from the image acquisition device; otherwise, the target object is not facing directly or facing away from the image acquisition device. Face away from the image acquisition device. In this way, target images facing directly or facing away from the image acquisition device can be screened out.
- the present disclosure does not limit the screening method.
- step S112 includes the following steps S112A to S112D, wherein:
- Step S112A acquiring key points of the plurality of objects
- Step S112B respectively determining the confidence of each key point of the object
- Step S112C according to the confidence of the key point, respectively determine the occlusion state of each of the objects;
- Step S112D Filter the multiple objects according to the occlusion state to obtain the target object.
- keypoints are detected as described in detail above.
- the confidence of each key point in the area can be determined, for example, the confidence of a certain key point is high, for example, when it is higher than the confidence threshold, Then the probability that the key point can be accurately detected is high.
- the confidence level of the key point of the shoulder is 99%, it can be considered that the detection accuracy of the key point is relatively high, and it can be used as the key point of the shoulder of the object. If the confidence level of a certain key is 10%, it is impossible to determine whether the detection of this key point is accurate. Determine the confidence of the key points of each object separately.
- the confidence of multiple key points is higher than the confidence threshold, it can be considered that multiple points of the object have been accurately detected.
- Key points for another example, if the confidence of some key points of an object is low, it is difficult to confirm whether the part of the key points is detected correctly. The reason may be that some areas of the object are blocked, resulting in inaccurate key point detection .
- the occlusion status of each object may be determined based on the confidence of each keypoint, e.g., one or more keypoints of an object have a low confidence, e.g., below a confidence threshold (e.g. , 0.2), then it can be considered that part of the object area is occluded, thereby excluding the object.
- a confidence threshold e.g. 0.2
- Fig. 3 is a schematic diagram of key points of objects in objects to be processed provided by an embodiment of the present disclosure. As shown in FIG. 3 , a part of the area of the person object 3 is exposed in the field of view of the image acquisition device, and key points 31 in this part of the area can be detected; another part of the area of the object 3 is blocked by an obstruction 32, causing the Key points in some areas are difficult to detect, or the detection results are inaccurate.
- eligible target objects can be selected, for example, target objects without occlusion and in a standing posture, or target objects without occlusion and facing directly or facing away from the image acquisition device.
- the present disclosure does not limit the above conditions.
- the preset feature points of the target object can be determined, that is, the feature points that can express the size information of the target object (for example, height or shoulder width, etc.) Feature points.
- step S12 may include the following steps S121 to S124, wherein:
- Step S121 acquiring a mask image of the target object
- Step S122 obtaining the covariance matrix of multiple pixels of the mask image
- Step S123 performing eigendecomposition on the covariance matrix to obtain eigenvectors
- Step S124 determining the preset feature points according to the feature vector and a plurality of pixel points of the mask image.
- the mask image of each target object is obtained, the mask image is an image representing the outline of the target object, for example, the outline of the target object is detected, and the pixel value of the pixel point within the outline is set to 1 , and the pixel values of the pixel points outside the outline are set to 0, so as to obtain the mask image of the target object.
- the present disclosure does not limit the pixel values of the pixel points of the mask image.
- the covariance matrix of multiple pixels of the mask image of the target object is obtained, for example, the mean value of the pixel values of each pixel point is determined, and the relationship between each pixel point is determined based on the mean value of the pixel values of each pixel point. covariance between. Since the mask image may include a plurality of pixels, a covariance matrix among the plurality of pixels may be obtained.
- the covariance matrix is subjected to eigendecomposition based on related techniques, for example, the covariance matrix is decomposed based on eigenvalues to obtain eigenvectors.
- the covariance matrix is decomposed based on eigenvalues to obtain eigenvectors.
- two sets of eigenvectors can be obtained, and these two sets of eigenvectors can respectively correspond to two sets of pixels in the mask image, and the pixels corresponding to each set of eigenvectors can form an axis, where, and Eigenvectors corresponding to larger eigenvalues form a longer axis, and eigenvectors corresponding to smaller eigenvalues form a shorter axis.
- the intersection of the longer axis and the contour line and the area inside the contour line in the mask image may be determined as a preset feature point.
- the preset feature point may be a feature point representing the height of the target object, that is, there are two intersection points, one of which is located at the top of the target object's head, and the other intersection point is located at the sole of the target object.
- FIG. 4A and FIG. 4B are schematic diagrams of a mask image of a target object in an image to be processed provided by an embodiment of the present disclosure.
- the intersection points of the longer axis and the contour line in the mask image are respectively located on the top of the head and the bottom of the feet of the target object, and these two intersection points can be used as preset feature points representing the height of the target object.
- the relationship between the two intersection points is the above-mentioned preset fixed distance relationship, that is, the distance between the two intersection points can be considered to be fixed, which is equal to the height of the target object.
- the longer axis 41 is perpendicular to the ground, and the distance between the intersection point 42 and the intersection point 43 is the height of the target object.
- the target object in Figure 4B due to factors such as shooting angles, the target object in the image is not facing the image acquisition device, so its visual effect is inclined, and its longer axis 41' is not perpendicular to the ground, but still The straight-line distance between the intersection point 42' and the intersection point 43' is the height of the target object, and the distance between the two intersection points is considered to be a fixed value.
- the preset feature points can also be obtained in other ways, for example, the top of the head and the bottom of the feet of the target object can be used as detection targets and directly detected by the neural network to obtain the preset feature points.
- the present disclosure does not limit the detection method of the preset feature points.
- a preset feature point representing the shoulder width of the target object can also be determined.
- the shoulder of the target object can be used as a detection target and detected by a neural network to obtain a preset feature point representing the shoulder width of the target object. point.
- the predetermined feature points can be determined by performing eigendecomposition on the covariance matrix, which can improve the detection accuracy.
- the matrix eigendecomposition method can reduce the amount of computation and reduce the occupancy of computing resources.
- a mapping matrix may be determined based on position information of preset feature points.
- the relationship between the preset feature points can be considered as a fixed relationship, for example, the distance relationship between the preset feature points on the top of the head and the bottom of the feet is a preset fixed distance relationship, that is, each target object can be considered Height is fixed.
- the distance relationship between the preset feature points on both shoulders can be considered as a preset fixed distance relationship, that is, the shoulder width of each target object can be considered to be fixed.
- the present disclosure does not limit the fixed relationship, for example, in addition to the distance relationship, the fixed relationship may also include an angle relationship and the like.
- the mapping matrix is a matrix used to represent the positional relationship between different preset feature points of the same target object, and can be used to reflect the fixed relationship.
- the distance between the top of the head and the soles of the feet can be set, that is, the height of each target object is fixed, for example, This fixed value can be set to 1.65 meters or 1.7 meters, etc., and the present disclosure does not limit the set value of the height of the target object.
- the distance between the preset feature points of the top of the head and the soles of each target object is the above-mentioned fixed value, and there is a fixed distance between the preset feature points between the top of the head and the soles of each target object.
- the mapping relationship can be expressed by the following formula (1-1):
- H is a mapping matrix representing the mapping relationship
- (u head , v head ) represents the coordinates of the preset feature points on the top of the head
- (u foot , v foot ) represents the coordinates of the preset feature points on the soles of the feet.
- mapping matrix may be calculated based on coordinates of preset feature points of multiple target objects.
- mapping matrix may be calculated through DLT (Direct Linear Transformation, direct linear transformation).
- the parameters in the mapping matrix are initial parameters. In practical applications, the mapping matrix can be optimized to determine internal reference information and pose information of the image acquisition device, that is, to calibrate the image acquisition device.
- the mapping matrix may also be determined based on the relationship between other preset feature points, for example, the mapping matrix may be determined in a similar manner based on the fixed distance between the preset feature points on both shoulders. The disclosure does not limit the manner of determining the mapping matrix.
- the parameters of the mapping matrix can be optimized based on the above determined mapping matrix and the coordinates of the preset feature points, so as to realize the calibration of the image acquisition device, that is, to obtain the image acquisition device Parameter information
- the parameter information may include internal parameter information and external parameter information (ie, posture information) of the image acquisition device.
- mapping matrix is a square matrix, that is, a matrix with the same number of rows and columns, which can be decomposed, for example, can be decomposed into the form of the following formula (1-2):
- h is the fixed distance between preset feature points, for example, the height of the target object;
- z is the installation height of the image acquisition device;
- P 0 P 1 P 2 is the first three columns of the projection matrix P of the image acquisition device
- the projection matrix P can be expressed as the product of the internal reference matrix K of the image acquisition device, the matrix translated by the three-dimensional translation vector t, and the rotation matrix R.
- the mapping matrix can be optimized to improve the accuracy of internal reference information and pose information.
- the optimization may be performed by presetting the coordinates of the feature points, so as to obtain accurate parameter information of the image acquisition device, for example, internal reference information and pose information.
- the above step S14 may include the following steps S141 to S143, wherein:
- Step S141 obtaining error information of the preset feature points according to the mapping matrix and the preset feature points;
- Step S142 adjusting parameters of the mapping matrix according to the error information to obtain an adjusted mapping matrix
- Step S143 according to the adjusted mapping matrix, obtain internal reference information and pose information of the image acquisition device.
- the error information of the preset feature points can be determined.
- the preset feature points on the top of the head The coordinates can be obtained by using the coordinates of the preset feature points on the soles of the feet and the transformation of the mapping matrix.
- the coordinates of the preset feature points on the soles of the feet will be transformed by the mapping matrix.
- the error can be reduced through optimization, so that the error of the parameters in the mapping matrix can be reduced to optimize the mapping matrix.
- optimization can be performed by the following optimization functions (1-3):
- P head is the coordinates of the preset feature points on the top of the head
- P foot is the coordinates of the preset feature points on the soles of the feet
- P' head H P foot , that is, P' head is the preset feature points on the soles of the feet through the mapping matrix Coordinates obtained after transformation
- P' foot H -1 P head , that is, P' foot is the coordinates obtained after transforming the preset feature points on the top of the head through the inverse matrix of the mapping matrix.
- the sum of the above-mentioned error information of all target objects is the formula (1-3), and the value of the formula (1-3) can be minimized to obtain a mapping matrix that minimizes the sum of the error information, namely Optimization of the mapping matrix can be achieved.
- the parameters of the mapping matrix can be adjusted according to the value of the above formula (1-3), for example, the parameters of the mapping matrix can be adjusted by methods such as the gradient descent method to gradually reduce the value of the formula (1-3) . After multiple adjustments, the value of the formula (1-3) does not continue to shrink, and the adjusted mapping matrix can be obtained.
- the minimum value of formula (1-3) may be determined by means of nonlinear programming, so as to determine the mapping matrix (that is, the adjusted mapping matrix) when the value of formula (1-3) reaches the minimum value. The present disclosure does not limit the adjustment method.
- the mapping matrix H is decomposed according to the formula (1-2), and the rotation matrix can be obtained , translation vector and internal reference matrix.
- the internal reference information and pose information of the image acquisition device can be obtained based on this.
- the internal reference information includes the focal length of the image acquisition device
- the pose information includes the height, pitch angle, yaw angle, and roll angle of the image acquisition device.
- an internal reference matrix may be obtained based on its decomposition result, and the parameters of the internal reference matrix may include the focal length of the image acquisition device.
- the height z of the image acquisition device can be obtained.
- the initial parameters of the image acquisition device can be set, for example, the position of the image acquisition device is the origin in the image to be processed, and its azimuth is the true north direction, so it can be obtained based on the decomposition of the mapping matrix
- the rotation matrix and translation vector of the image acquisition device to obtain the pitch angle, roll angle and yaw angle.
- the disclosure does not limit the parameters included in the internal reference information and pose information.
- the mapping matrix corresponding to the preset feature points of both shoulders may also be optimized in the above manner, and then the above internal parameter information and pose information are obtained. It is also possible to directly use the initial parameters of the mapping matrix obtained in step S13 to solve the internal reference information and pose information without performing the above optimization steps, but the error of the obtained parameters will be higher than the error of the optimized parameters. This disclosure does not limit this.
- the preset feature points can be determined through the eigendecomposition of the matrix, and then the mapping matrix can be determined by using the preset feature points, and finally the internal reference information of the image acquisition device can be determined according to the mapping matrix and the preset feature points And pose information, without the need for the same target object to appear in multiple preset positions, and without the cooperation of the target object, the self-calibration process can be completed, which reduces the manual workload and calibration cost, and is applicable to a large number of image acquisition devices and wide distribution In the scene, for example, it is suitable for the self-calibration of many cameras in the urban monitoring system; in addition, the mapping matrix can also be optimized to reduce the error of the mapping matrix and improve the detection accuracy.
- Fig. 5 is a schematic diagram of the application of the calibration method provided by the embodiment of the present disclosure.
- the image acquisition device 51 is any monitoring camera in the monitoring system of the city, and a plurality of person objects 52 appear in the field of view of the image acquisition device 51, when the image acquisition device 51 is calibrated, it can be The video taken by the image acquisition device 51 is acquired, and the image to be processed of the object 52 existing therein is determined.
- the objects in the image to be processed may be screened, so as to screen out target objects whose poses are standing and not occluded.
- the key points of each object can be detected, and the posture of each object can be determined based on the key points to exclude non-standing objects; it can also be determined based on the confidence of the key points Objects that are occluded, so as to obtain the target object whose pose is standing and not occluded.
- preset feature points of each target object are acquired, for example, preset feature points representing the height of the target object, that is, preset feature points of the top of the head and soles of the feet.
- the longer axis of , the intersection of the axis and the contour of the target object in the mask image is the preset feature point of the top of the head and the bottom of the feet.
- the height of the target object is set as a fixed value, and a mapping matrix representing the mapping relationship of each target object is obtained based on the fixed value, as shown in formula (1-1).
- the initial parameters of the mapping matrix can be solved. There may be errors in the initial parameters, which can be optimized to minimize the errors.
- the mapping matrix is optimized by formula (1-3) to obtain a mapping matrix with minimized errors. Then based on this mapping matrix, internal reference information and pose information are obtained. For example, the mapping matrix is decomposed according to formula (1-2) to obtain the internal reference matrix, rotation matrix and translation vector of the image acquisition device. Based on the parameters of the internal reference matrix, the internal reference information such as focal length can be obtained, and the attitude information such as pitch angle, yaw angle, and roll angle can be obtained based on the rotation matrix and translation vector. Based on the decomposition results of formula (1-2), the image acquisition device can be obtained Attitude information such as altitude.
- the calibration method can be used for self-calibration of surveillance cameras in a city surveillance system with a large number of surveillance cameras and wide distribution, so as to reduce the workload of manual calibration for each surveillance camera. It can also be used in other camera calibration scenarios, and the present disclosure does not limit the applicable field of the calibration method.
- the present disclosure also provides calibration devices, electronic equipment, computer-readable storage media, and computer program products, all of which can be used to implement any of the calibration methods provided in the present disclosure.
- calibration devices electronic equipment, computer-readable storage media, and computer program products, all of which can be used to implement any of the calibration methods provided in the present disclosure.
- Fig. 6 is a block diagram of a calibration device provided by an embodiment of the present disclosure.
- the device includes: a target object acquisition part 61 configured to detect the image to be processed and acquire the target object in the image to be processed; a feature point determination part 62 configured to determine the target object The preset feature points of the object; the mapping matrix determining part 63 is configured to determine a mapping matrix corresponding to the preset feature points according to the preset feature points; the parameter information determining part 64 is configured to determine according to the mapping The matrix and the preset feature points are used to obtain parameter information of an image acquisition device, and the image to be processed is acquired by the image acquisition device.
- the target object acquisition part is further configured to: detect a plurality of objects in the image to be processed; At least one of the steps is to screen the multiple objects to obtain the target object.
- the target object acquiring part is further configured to: acquire the key points of the plurality of objects; respectively determine the pose of each of the objects according to the key points of each of the objects; poses of each of the objects, and screen the multiple objects to obtain the target object.
- the target object acquiring part is further configured to: acquire the key points of the plurality of objects; respectively determine the confidence of the key points of each of the objects; according to the confidence of the key points , respectively determine the occlusion state of each of the objects; and filter the plurality of objects according to the occlusion state to obtain the target object.
- the target object obtaining part is further configured to: obtain position information of each of the objects; and obtain key points of the plurality of objects according to the position information of each of the objects.
- the feature point determination part is further configured to: acquire a mask image of the target object; acquire a covariance matrix of multiple pixels of the mask image; Perform feature decomposition to obtain feature vectors; determine the preset feature points according to the feature vectors and multiple pixels of the mask image.
- the parameter information includes internal reference information and pose information
- the parameter information determining part is further configured to: obtain the preset feature points according to the mapping matrix and the preset feature points According to the error information, the parameters of the mapping matrix are adjusted to obtain an adjusted mapping matrix; according to the adjusted mapping matrix, internal reference information and pose information of the image acquisition device are obtained.
- the mapping matrix is a matrix used to represent the positional relationship between different preset feature points of the same target object.
- a "part" may be a part of a circuit, a part of a processor, a part of a program or software, etc., of course it may also be a unit, a module or a non-modular one.
- the functions or modules included in the calibration device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments, and for specific implementation, refer to the descriptions of the above method embodiments.
- Embodiments of the present disclosure also provide a computer-readable storage medium, on which computer program instructions are stored, and the above-mentioned method is implemented when the computer program instructions are executed by a processor.
- the computer readable storage medium may be a non-volatile computer readable storage medium.
- An embodiment of the present disclosure also proposes an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to invoke the instructions stored in the memory to execute the above method.
- An embodiment of the present disclosure also provides a computer program product, including computer readable codes.
- the processor in the device executes instructions for implementing the calibration method provided in any of the above embodiments. .
- the embodiments of the present disclosure also provide another computer program product, which is used for storing computer-readable instructions.
- the computer executes the operation of the calibration method provided by any of the above-mentioned embodiments.
- Electronic devices may be provided as terminals, servers, or other forms of devices.
- FIG. 7 is a block diagram of an electronic device 700 provided by an embodiment of the present disclosure.
- the electronic device 700 may be a terminal such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, or a personal digital assistant.
- electronic device 700 may include one or more of the following components: processing component 702, memory 704, power supply component 706, multimedia component 708, audio component 710, input/output (I/O) interface 712, sensor component 714 , and the communication component 716.
- the processing component 702 generally controls the overall operations of the electronic device 700, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
- the processing component 702 may include one or more processors 718 to execute instructions to complete all or part of the steps of the above method.
- processing component 702 may include one or more modules that facilitate interaction between processing component 702 and other components.
- processing component 702 may include a multimedia module to facilitate interaction between multimedia component 708 and processing component 702 .
- the memory 704 is configured to store various types of data to support operations at the electronic device 700 . Examples of such data include instructions for any application or method operating on the electronic device 700, contact data, phonebook data, messages, pictures, videos, and the like.
- Memory 704 can be realized by any type of volatile or non-volatile storage device or their combination, such as Static Random-Access Memory (Static Random-Access Memory, SRAM), Electrically Erasable Programmable Read-Only Memory (Electrically Erasable Programmable read only memory, EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), programmable read-only memory (Programmable Read-only memory, PROM), read-only memory (Read-only memory , ROM), magnetic memory, flash memory, magnetic disk or optical disk.
- Static Random-Access Memory SRAM
- Electrically Erasable Programmable Read-Only Memory Electrically Erasable Programmable Read-Only Memory
- EEPROM Electrically Erasable
- the power supply component 706 provides power to various components of the electronic device 700 .
- Power components 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for electronic device 700 .
- the multimedia component 708 includes a screen providing an output interface between the electronic device 700 and the user.
- the screen may include a liquid crystal display (Liquid Crystal Display, LCD) and a touch panel (TouchPanel, TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user.
- the touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may not only sense an edge of a touch or slide action, but also detect a duration and pressure associated with the touch or slide operation.
- the multimedia component 708 includes a front camera and/or a rear camera. When the electronic device 700 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capability.
- the audio component 710 is configured to output and/or input audio signals.
- the audio component 710 includes a microphone (microphone, MIC), and when the electronic device 700 is in an operation mode, such as a calling mode, a recording mode and a voice recognition mode, the microphone is configured to receive an external audio signal. Received audio signals may be stored in memory 704 or sent via communication component 716 .
- the audio component 710 also includes a speaker for outputting audio signals.
- the I/O interface 712 provides an interface between the processing component 702 and a peripheral interface module, which may be a keyboard, a click wheel, a button, and the like. These buttons may include, but are not limited to: a home button, volume buttons, start button, and lock button.
- Sensor assembly 714 includes one or more sensors for providing status assessments of various aspects of electronic device 700 .
- the sensor component 714 can detect the open/closed state of the electronic device 700, the relative positioning of components, such as the display and the keypad of the electronic device 700, the sensor component 714 can also detect the electronic device 700 or one of the electronic device 700 Changes in position of components, presence or absence of user contact with electronic device 700 , electronic device 700 orientation or acceleration/deceleration and temperature changes in electronic device 700 .
- Sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact.
- Sensor assembly 714 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
- the sensor component 714 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.
- the communication component 716 is configured to facilitate wired or wireless communication between the electronic device 700 and other devices.
- the electronic device 700 can access a wireless network based on communication standards, such as WiFi, 2G or 3G, or a combination thereof.
- the communication component 716 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
- the communication component 716 also includes a near field communication (Near Field Communication, NFC) module to facilitate short-range communication.
- the NFC module can be based on Radio Frequency Identification (RFID) technology, Infrared Data Association (Infrared Data Association, IrDA) technology, Ultra Wide Band (UWB) technology, Bluetooth (bluetooth, BT) technology and other technology to achieve.
- RFID Radio Frequency Identification
- IrDA Infrared Data Association
- UWB Ultra Wide Band
- Bluetooth bluetooth, BT
- the electronic device 700 may be implemented by one or more application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), digital signal processors (Digital Signal Processing, DSP), digital signal processing equipment (Digital Signal Processing Device, DSPD), Programmable Logic Device (Programmable Logic Device, PLD), Field Programmable Gate Array (Field Programmable Gate Array, FPGA), controller, microcontroller, microprocessor or other electronic components are implemented for performing the above method .
- ASIC Application Specific Integrated Circuit
- DSP Digital Signal Processing
- DSPD digital signal processing equipment
- PLD Programmable Logic Device
- FPGA Field Programmable Gate Array
- controller microcontroller, microprocessor or other electronic components are implemented for performing the above method .
- non-volatile computer-readable storage medium such as the memory 704 including computer program instructions, which can be executed by the processor 718 of the electronic device 700 to implement the above method.
- FIG. 8 is a block diagram of an electronic device 800 provided by an embodiment of the present disclosure.
- the electronic device 800 may be provided as a server.
- electronic device 800 includes processing component 802 , which also includes one or more processors, and a memory resource represented by memory 804 for storing instructions executable by processing component 802 , such as application programs.
- the application program stored in memory 804 may include one or more modules each corresponding to a set of instructions.
- the processing component 802 is configured to execute instructions to perform the above method.
- the electronic device 800 may also include a power supply component 806 configured to perform power management of the electronic device 800, a wired or wireless network interface 808 configured to connect the electronic device 800 to a network, and an input-output (I/O) interface 810 .
- the electronic device 800 can operate based on an operating system stored in the memory 804, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.
- non-volatile computer-readable storage medium such as a memory 804 including computer program instructions, which can be executed by the processing component 802 of the electronic device 800 to complete the above method.
- the storage medium may be a volatile or non-volatile computer-readable storage medium.
- the present disclosure can be a system, method and/or computer program product.
- a computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.
- a computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device.
- a computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
- Computer-readable storage media include: portable computer disks, hard disks, Random Access Memory (RAM), Read-Only Memory (ROM), computer Erasable Programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM or flash memory), Static Random Access Memory (Static Random Access Memory, SRAM), Portable Compact Disc Read-Only Memory (CD- ROM), digital versatile disk (Digital Versatile Disc, DVD), memory stick, floppy disk, mechanically encoded devices, such as punched cards or raised structures in grooves on which instructions are stored, and any suitable combination of the foregoing.
- RAM Random Access Memory
- ROM Read-Only Memory
- EPROM Erasable Programmable Read-Only Memory
- EPROM or flash memory Static Random Access Memory
- Static Random Access Memory SRAM
- Portable Compact Disc Read-Only Memory CD- ROM
- digital versatile disk Digital Versatile Disc, DVD
- memory stick floppy disk
- mechanically encoded devices such as punched cards or raised structures in grooves on which instructions
- computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or transmitted electrical signals.
- Computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or downloaded to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
- the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
- a network adapter card or a network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
- Computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or Source or object code written in any combination, including object-oriented programming languages—such as Smalltalk, C++, etc., and conventional procedural programming languages—such as the “C” language or similar programming languages.
- Computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
- the remote computer can be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computer such as use an Internet service provider to connect via the Internet).
- electronic circuits such as programmable logic circuits, field programmable gate arrays (Field Programmable Gate Array, FPGA) or programmable logic arrays (PLA) are personalized by utilizing state information of computer readable program instructions , the electronic circuit can execute computer-readable program instructions, thereby implementing various aspects of the present disclosure.
- These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that when executed by the processor of the computer or other programmable data processing apparatus , producing an apparatus for realizing the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
- These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause computers, programmable data processing devices and/or other devices to work in a specific way, so that the computer-readable medium storing instructions includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks in flowcharts and/or block diagrams.
- each block in a flowchart or block diagram may represent a module, a portion of a program segment, or an instruction that includes one or more Executable instructions.
- the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
- each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified function or action , or may be implemented by a combination of dedicated hardware and computer instructions.
- the computer program product can be specifically realized by means of hardware, software or a combination thereof.
- the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK) etc. wait.
- a software development kit Software Development Kit, SDK
- Embodiments of the present disclosure provide a calibration method and device, electronic equipment, a storage medium, and a computer program product, wherein the calibration method includes: detecting an image to be processed, acquiring a target object in the image to be processed; determining the target object in the image to be processed The preset feature points of the target object; according to the preset feature points, determine the mapping matrix corresponding to the preset feature points; according to the mapping matrix and the preset feature points, obtain the parameter information of the image acquisition device, and the The image to be processed is acquired by the image acquisition device.
- the mapping matrix can be determined through the preset feature points in the area where any target object is located in the image, and then the internal reference information and pose information of the image acquisition device can be determined, without the need for the same target object to appear in multiple
- the preset position can complete the self-calibration process without the cooperation of the target object.
- the manual workload and calibration cost are reduced, and it can be applied to scenes with a large number of image acquisition devices and wide distribution.
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Abstract
本公开涉及一种标定方法及装置、电子设备、存储介质及计算机程序产品,所述方法包括:对待处理图像进行检测,获取所述待处理图像中的目标对象;确定所述目标对象的预设特征点;根据所述预设特征点,确定与所述预设特征点对应的映射矩阵;根据所述映射矩阵以及所述预设特征点,获得图像获取设备的参数信息,所述待处理图像是所述图像获取设备获取的。
Description
相关申请的交叉引用
本公开实施例基于申请号为202111497801.X、申请日为2021年12月09日、申请名称为“标定方法及装置、电子设备和存储介质”的中国专利申请提出,并要求该中国专利申请的优先权,该中国专利申请的全部内容在此引入本公开作为参考。
本公开涉及但不限于计算机视觉技术领域,尤其涉及一种标定方法及装置、电子设备、存储介质及计算机程序产品。
监控***是安防***中应用最多的***之一,根据监控***中的监控相机的相机参数可以从被监控的场景中获取多种信息,例如,行人的具***置、身高、行走速度等。但是,城市中的监控相机数量多、分布广,导致监控相机通常难以获取相机参数。因此,亟需一种低成本的相机标定方法。
发明内容
本公开实施例提出了一种标定方法及装置、电子设备、存储介质及计算机程序产品。
本公开实施例提供了一种标定方法,包括:对待处理图像进行检测,获取所述待处理图像中的目标对象;确定所述目标对象的预设特征点;根据所述预设特征点,确定与所述预设特征点对应的映射矩阵;根据所述映射矩阵以及所述预设特征点,获得图像获取设备的参数信息,所述待处理图像是所述图像获取设备获取的。
根据本公开的实施例的标定方法,可通过图像中任意目标对象所在区域的预设特征点来确定映射矩阵,进而确定图像获取设备的内参信息和位姿信息,无需同一目标对象出现在多个预设位置,也无需目标对象配合,即可完成自标定过程,降低了人工工作量和标定成本,可适用于图像获取设备数量多,分布广的场景中,例如,适用于城市监控***的众多相机的自标定中。
本公开实施例还提供了一种标定装置,包括:目标对象获取部分,被配置为对待处理图像进行检测,获取所述待处理图像中的目标对象;特征点确定部分,被配置为确定所述目标对象的预设特征点;映射矩阵确定部分,被配置为根据所述预设特征点,确定与所述预设特征点对应的映射矩阵;参数信息确定部分,被配置为根据所述映射矩阵以及所述预设特征点, 获得图像获取设备的参数信息,所述待处理图像是所述图像获取设备获取的。
本公开实施例还提供了一种电子设备,包括:处理器;用于存储处理器可执行指令的存储器;其中,所述处理器被配置为调用所述存储器存储的指令,以执行上述方法。
本公开实施例还提供了一种计算机可读存储介质,其上存储有计算机程序指令,所述计算机程序指令被处理器执行时实现上述方法。
本公开实施例还提供了一种计算机程序产品,该计算机程序产品包括计算机程序或指令,在所述计算机程序或指令在电子设备上运行的情况下,使得所述电子设备执行上述方法。
应当理解的是,以上的一般描述和后文的细节描述仅是示例性和解释性的,而非限制本公开。根据下面参考附图对实施例的详细说明,本公开的其它特征及方面将变得清楚。
此处的附图被并入说明书中并构成本说明书的一部分,这些附图示出了符合本公开的实施例,并与说明书一起用于说明本公开的技术方案。
图1为本公开实施例提供的标定方法的流程示意图;
图2A为本公开实施例提供的待处理图像中的对象的关键点的示意图;
图2B为本公开实施例提供的待处理图像中的对象的关键点的示意图;
图3为本公开实施例提供的待处理图像中的对象的关键点的示意图;
图4A为本公开实施例提供的待处理图像中目标对象的掩膜图像的示意图;
图4B为本公开实施例提供的待处理图像中目标对象的掩膜图像的示意图;
图5为本公开实施例提供的标定方法的应用示意图;
图6为本公开实施例提供的标定装置的组成结构框图;
图7为本公开实施例提供的一种电子设备的框图;
图8为本公开实施例提供的一种电子设备的框图。
以下将参考附图详细说明本公开的各种实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面,但是除非特别指出,不必按比例绘制附图。
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。
本文中术语“和/或”,是一种描述关联对象的关联关系,表示可以存在三种关系,例如, A和/或B,可以表示:单独存在A,同时存在A和B,单独存在B这三种情况。另外,本文中术语“至少一种”表示多种中的任意一种或多种中的至少两种的任意组合,例如,包括A、B、C中的至少一种,可以表示包括从A、B和C构成的集合中选择的任意一个或多个元素。
另外,为了更好地说明本公开,在下文的具体实施方式中给出了众多的具体细节。本领域技术人员应当理解,没有某些具体细节,本公开同样可以实施。在一些实例中,对于本领域技术人员熟知的方法、手段、元件和电路未作详细描述,以便于凸显本公开的主旨。
相关技术中,对监控相机进行标定包括以下两种方法:
第一种,标定板标定。标定板标定需要人工将标定板放在监控相机的视野中,并获取对应时间的摄像头视频流。但是,由于城市中摄像头覆盖范围广,该方法操作繁琐耗时,所需人工成本巨大,在城市级场景中不适用;
第二种,相机自标定。相机自标定是通过检测出视频中多个人在图片不同位置头和脚的位置,通过几何方法估计相机灭点,从而计算相机参数。相机自标定方法要求同一个人在图片不同位置都有抓拍,并且相机参数估计结果的鲁棒性较低,例如,如果图像获取设备发生移动,则其标定的参数则可能失效,需要重新进行标定。
基于此,本公开实施例提供了一种标定方法,下面将结合本公开实施例中的附图,对本公开实施例中的技术方案进行清楚、完整地描述。
图1为本公开实施例提出的一种标定方法的流程示意图,如图1所示,所述方法包括步骤S11至步骤S14,其中:
步骤S11,对待处理图像进行检测,获取所述待处理图像中的目标对象;
步骤S12,确定所述目标对象的预设特征点;
步骤S13,根据所述预设特征点,确定与所述预设特征点对应的映射矩阵;
步骤S14,根据所述映射矩阵以及所述预设特征点,获得图像获取设备的参数信息,所述待处理图像是所述图像获取设备获取的。
根据本公开的实施例的标定方法,通过待处理图像中任意目标对象所在区域的预设特征点来确定映射矩阵,进而根据映射矩阵和预设特征点确定图像获取设备的参数信息。因此,本公开的实施例提出的标定方法无需同一目标对象出现在图像获取设备视野中的多个预设位置,也无需目标对象配合,即可完成自标定过程,从而降低了人工工作量和标定成本,适用于图像获取设备数量多、分布广的场景中,例如,适用于城市监控***的众多相机的自标定中。
在一些实现方式中,基于图像获取设备获取的待处理图像中的目标对象的预设特征点来进行标定,其中,预设特征点可以是目标对象所在区域的像素点中能够表示目标对象尺寸的像素点(或特征点),例如可以是目标对象的头顶和脚底的像素点,或者目标对象双肩两端的像素点等等。本公开对预设特征点可以是目标对象的哪个部位的像素点不做限制。在一些实现方式中,获取目标对象的预设特征点,根据预设特征点之间的关系(例如,目标对象的头顶和脚底之间的距离(即,目标对象的身高)是固定的,因此,两个点之间具有特定的距离关系)获取映射矩阵,进而基于映射矩阵来求解图像获取设备的内参信息和位姿信息,即,对图像获取设备进行自动标定。
在一些实现方式中,对于任意图像获取设备(例如,监控***中的监控相机),该图像获取设备可设置在任意位置,以拍摄该图像获取设备视野范围内的视频。在进行图像获取设备的自标定时,获得该图像获取设备拍摄的视频中的多个视频帧,并在该多个视频帧中确定存在对象(例如,有行人经过)的视频帧以作为待处理图像。
在一些实现方式中,由于自标定时需要确定目标对象的预设特征点之间的关系,例如,目标对象的头顶和脚底之间的距离关系,进而基于该关系获得映射矩阵来进行标定。因此,需要在待处理图像中的的多个对象中确定合格的对象以作为目标对象,即,基于目标对象所在区域的像素点能够确定出该目标对象的预设特征点,且能够基于预设特征点来确定上述映射矩阵。在一些实现方式中,如果所述映射矩阵是基于目标对象的头顶和脚底之间的距离确定的,则可在多个对象中筛选出头顶和脚底均能够被检测到(未被遮挡),且其姿势为站姿(即,头顶和脚底之间的距离关系符合预设的固定距离关系,不会由于目标对象的姿态使该距离与预设的固定距离之间的差距过大)的对象作为目标对象。在一些实现方式中,如果所述映射矩阵是基于目标对象的双肩之间的距离确定的,则可在多个对象中筛选出正对或背对图像获取设备(即,双肩之间的距离关系符合预设的固定距离关系,不会由于目标对象的角度使该距离与预设的固定距离之间的差距过大)且双肩均未被遮挡的对象作为目标对象。这样,通过对待处理图像中的对象进行筛选以获得合格的对象作为目标对象,不仅可以提高标定的准确度,还可以减少下一个图像处理步骤的计算量,提高对图像获取设备进行标定的标定效率。
在一些实现方式中,在步骤S11中,在进行目标对象检测时,可基于上述因素进行检测,即,该步骤S11可包括步骤S111和步骤S112,其中:
步骤S111,在所述待处理图像中检测出多个对象;
步骤S112,根据所述多个对象的姿态和所述多个对象的遮挡状态中的至少之一,对所述 多个对象进行筛选,获得所述目标对象,即,可确定待处理图像中多个对象的姿态和遮挡状态中的至少一种,并基于姿态和遮挡状态中的至少之一进行筛选,例如,筛选出站姿,且未被遮挡的对象作为目标对象,或者,筛选出正对或背对图像获取设备,且未被遮挡的对象作为目标对象。
在一些实现方式中,在进行筛选时,可检测各对象的关键点,并基于关键点来获得上述姿态和遮挡状态中的至少之一,以对多个对象进行筛选。因此,步骤S112包括步骤S1121至步骤S1123,其中:
步骤S1121,获取所述多个对象的关键点;
步骤S1122,根据每个所述对象的关键点,确定每个所述对象的姿态;
步骤S1123,根据每个所述对象的姿态,对所述多个对象进行筛选,获得所述目标对象。
在一些实现方式中,各对象的关键点可以是表示目标对象的身体结构的关键点,例如,所述关键点可包括头部关键点,肩部关键点,肘部关键点,手部关键点,腰部关键点,膝部关键点,脚部关键点等。本公开对各对象的关键点的类型和位置不做限制。
在一些实现方式中,步骤S1121可以包括步骤S1121A和步骤S1121B,其中:
步骤S1121A,获得每个所述对象的位置信息;
步骤S1121B,根据每个所述对象的位置信息,获得所述多个对象的关键点。
在一些实现方式中,可通过深度学习神经网络对待处理图像进行检测,获得多个对象的位置信息,本公开对检测方法不做限制。所述位置信息可表示各对象的位置,例如,所述位置信息可以是将对象进行框选的检测框,可以是描绘对象轮廓的轮廓线,还可以是表示目标对象的关键点的坐标信息等。本公开对位置信息的具体形式不做限制。
在一些实现方式中,可根据各对象的位置信息,获得各对象的关键点,例如,当所述位置信息是将对象进行框选的检测框时,在检测各对象的关键点时,可对各检测框中的图像块进行关键点检测,以获得各对象的关键点。这样,仅对检测框中的图像块进行关键点检测,无需全图检测,可降低关键点检测处理的运算量。或者,当所述位置信息是描绘对象轮廓的轮廓线时,则可仅对轮廓线内部的区域进行检测,获取各对象的关键点,该方式也可降低关键点检测的运算量。在一些实现方式中,可通过深度学习神经网络进行关键点检测。本公开对关键点检测的具体方法不做限制。
在一些实现方式中,在获取各对象的关键点后,可基于关键点来筛选出符合条件的目标对象。例如,当通过头顶和脚底之间的距离来确定映射矩阵时,可筛选出头顶和脚底均能够被检测到(即,未被遮挡),且其姿势为站姿的目标对象。又例如,当通过双肩之间的距离 来确定映射矩阵时,可筛选出双肩均能够被检测到(即,未被遮挡),且正对或背对图像获取设备的目标对象。
在一些实现方式中,可通过关键点之间连线的角度来确定各对象的姿态。例如,上身关键点连线与大腿关键点连线之间的角度较大时,可认为该对象的姿态并非站姿,例如,可判断该对象的姿态为坐姿或躬着身体等姿态。又例如,可判断肩部关键点和腰部关键点之间的连线,与腰部关键点和膝部关键点之间的连线的角度,如果该角度较大(例如,大于或等于30°等角度阈值),则可认为该对象的姿态为非站姿。
图2A和图2B均为本公开实施例提出的待处理图像中的对象的关键点的示意图。如图2A所示,目标对象的肩部关键点和腰部关键点之间的连线21,与腰部关键点和膝部关键点之间的连线22的角度较小,例如,小于30°,则可认为该对象的姿态为站姿。如图2B所示,目标对象的肩部关键点和腰部关键点之间的连线23,与腰部关键点和膝部关键点之间的连线24的角度较大,例如,大于30°,则可认为该对象的姿态为非站姿。
在一些实现方式中,可基于上述方式确定的各对象的姿态,从而对多个对象进行筛选,将姿态为非站姿的对象排除。
在一些实现方式中,还可通过其他方式来确定对象的姿态,在一些实现方式中,当需要筛选出正对或背对图像获取设备的目标对象时,可基于目标对象的行进方向等方式来筛选,例如,如果目标对象的行进方向为平行于待处理图像的高度方向(例如,Y轴方向),则该目标对象为正对或背对图像获取设备,否则,该目标对象不是正对或背对图像获取设备。这样,可筛选出正对或背对图像获取设备的目标图像。本公开对筛选方式不做限制。
在一些实现方式中,除了确定各对象的姿态外,还可确定各对象是否被遮挡,如果被遮挡,则可能无法获得该对象的预设特征点。在一些实现方式中,可利用各对象的关键点来确定各对象是否被遮挡。上述步骤S112包括以下步骤S112A至步骤S112D,其中:
步骤S112A,获取所述多个对象的关键点;
步骤S112B,分别确定每个所述对象的关键点的置信度;
步骤S112C,根据所述关键点的置信度,分别确定每个所述对象的遮挡状态;
步骤S112D,根据所述遮挡状态,对所述多个对象进行筛选,获得所述目标对象。
在一些实现方式中,关键点的检测方式如上文所详细描述的。在利用深度学习神经网络对各对象所在区域进行关键点检测时,可确定该区域中各关键点的置信度,例如,某个关键点的置信度较高,例如,高于置信度阈值时,则该关键点能够被准确检测的概率较高。在一些实现方式中,肩部的关键点的置信度为99%,则可认为该关键点检测准确率较高,可作为 该对象的肩部关键点。如果某个关键的置信度为10%,则无法确定对于该关键点的检测是否准确。分别确定各对象的关键点的置信度,例如,针对某个未被遮挡的对象,其多个关键点的置信度均高于置信度阈值,则可认为已准确地检测到该对象的多个关键点;又例如,某个对象,其部分关键点的置信度较低,则难以确认所述部分关键点是否检测正确,其原因可能是该对象的部分区域被遮挡,造成关键点检测不准确。
在一些实现方式中,可基于各关键点的置信度来确定各对象的遮挡状态,例如,某个对象的一个或更多个关键点的置信度较低,例如,低于置信度阈值(例如,0.2),则可认为该对象的部分区域被遮挡,从而将该对象排除。
图3为本公开实施例提供的待处理对象中的对象的关键点的示意图。如图3所示,该人物对象3有部分区域暴露在图像获取设备的视野内,该部分区域内的关键点31能够被检测到;该对象3的另一部分区域被遮挡物32遮挡,造成该部分区域的关键点难以被检测到,或者检测结果不准确。
在一些实现方式中,通过上述筛选工作,可选择出符合条件的目标对象,例如,无遮挡且姿态为站姿的目标对象,或者,无遮挡且正对或背对图像获取设备的目标对象。本公开对上述条件不做限制。
在一些实现方式中,基于上述符合条件的目标对象所在区域的多个像素点,可确定目标对象的预设特征点,即,可表达目标对象的尺寸信息(例如,身高或肩宽等)的特征点。
在一些实现方式中,步骤S12可包括以下步骤S121至步骤S124,其中:
步骤S121,获取所述目标对象的掩膜图像;
步骤S122,获取所述掩膜图像的多个像素点的协方差矩阵;
步骤S123,对所述协方差矩阵进行特征分解,获得特征向量;
步骤S124,根据所述特征向量和所述掩膜图像的多个像素点,确定所述预设特征点。
在一些实现方式中,获取各目标对象的掩膜图像,所述掩膜图像为表示目标对象轮廓的图像,例如,检测目标对象的轮廓,将该轮廓之内的像素点的像素值设置为1、轮廓之外的像素点的像素值设置为0,从而获得所述目标对象的掩膜图像。本公开对掩膜图像的像素点的像素值不做限制。
在一些实现方式中,获取目标对象的掩膜图像的多个像素点的协方差矩阵,例如,确定各像素点的像素值的均值,并基于各像素点的像素值的均值确定各像素点之间的协方差。由于掩膜图像可包括多个像素点,因此可获得多个像素点互相之间的协方差矩阵。
在一些实现方式中,基于相关技术对该协方差矩阵进行特征分解,例如,基于特征值分 解该协方差矩阵,获得特征向量。在分解协方差矩阵过程中,可获得两组特征向量,这两组特征向量可分别对应于掩膜图像中的两组像素点,每组特征向量对应的像素点可组成一条轴线,其中,与较大特征值对应的特征向量可组成一条较长的轴线,与较小的特征值对应的特征向量可组成一条较短的轴线。
在一些实现方式中,所述较长的轴线与掩膜图像中轮廓线上及轮廓线内区域的交点可确定为预设特征点。该预设特征点可以是表示目标对象身高的特征点,即,所述交点有两个,其中一个交点所在位置为目标对象的头顶,另一个交点所在位置为目标对象的脚底。
图4A和图4B均为本公开实施例提供待处理图像中目标对象的掩膜图像的示意图。如图4A和图4B所示,较长的轴线与掩膜图像中轮廓线上的交点分别位于目标对象的头顶和脚底,这两个交点可作为表示目标对象身高的预设特征点。所述两个交点之间的关系为上述预设的固定距离关系,即,所述两个交点之间的距离可认为是固定的,与目标对象的身高相等。例如,如图4A中的目标对象,所述较长的轴线41垂直于地面,所述交点42与交点43之间的距离为该目标对象的身高。如图4B中的目标对象,由于拍摄角度等因素,图像中目标对象并未正对图像获取设备,因此其视觉效果是倾斜的,其较长的轴线41’并未垂直于地面,但仍以交点42’与交点43’之间的直线距离为该目标对象的身高,且认为两个交点之间的距离是固定值。
在一些实现方式中,还可通过其他方式获得预设特征点,例如,可将目标对象的头顶和脚底两个点作为检测目标,直接通过神经网络进行检测,以获得预设特征点。本公开对预设特征点的检测方式不做限制。在一些实现方式中,还可确定表示目标对象肩宽的预设特征点,例如,可将目标对象的肩部作为检测目标,通过神经网络进行检测,以获得表示目标对象肩宽的预设特征点。
这样,通过对协方差矩阵进行特征分解的方式确定预设特征点,可提升检测准确性,且矩阵特征分解的方式相对于深度学习的方式,可减小运算量,降低运算资源占用。
在一些实现方式中,在步骤S13中,可基于预设特征点的位置信息,确定映射矩阵。如上所述,可认为预设特征点之间的关系为固定的关系,例如,头顶和脚底的预设特征点之间的距离关系为预设的固定距离关系,即,可认为各目标对象的身高是固定的。或者,可认为双肩的预设特征点之间的距离关系为预设的固定距离关系,即,可认为各目标对象的肩宽是固定的。本公开对固定的关系不做限制,例如,除距离关系外,所述固定的关系还可包括角度关系等。所述映射矩阵为用于表示同一目标对象的不同预设特征点之间的位置关系的矩阵,可用于反映所述固定的关系。
在一些实现方式中,以头顶和脚底的预设特征点为例,在求解映射矩阵时,可设定头顶和脚底之间的距离,即,每个目标对象的身高都是固定的,例如,可将此固定值设定为1.65米或1.7米等,本公开对目标对象的身高的设定值不做限制。
在一些实现方式中,每个目标对象的头顶和脚底的预设特征点之间的距离均为上述固定值,则每个目标对象的头顶和脚底之间的预设特征点之间均具有固定的映射关系,所述映射关系可通过以下公式(1-1)表示:
其中,H为表示所述映射关系的映射矩阵,(u
head,v
head)表示头顶的预设特征点的坐标,(u
foot,v
foot)表示脚底的预设特征点的坐标。
在一些实现方式中,可确定多个目标对象(例如,大于或等于4个)的预设特征点的坐标,并分别根据公式(1-1)确定其映射关系。在一些实现方式中,可基于多个目标对象的预设特征点的坐标,计算所述映射矩阵。在一些实现方式中,可通过DLT(Direct Linear Transformation,直接线性变换)计算所述映射矩阵。该映射矩阵中的参数为初始参数,实际应用时,可对映射矩阵进行优化,以确定图像获取设备的内参信息和位姿信息,即,标定图像获取设备。
在一些实现方式中,也可基于其他预设特征点之间的关系确定映射矩阵,例如,可基于双肩的预设特征点之间的固定距离以类似的方式确定映射矩阵。本公开对确定映射矩阵的方式不作限制。
在一些实现方式中,在步骤S14中,可基于以上确定的映射矩阵,以及预设特征点的坐标,来优化映射矩阵的参数,以实现对图像获取设备的标定,即,获得图像获取设备的参数信息,所述参数信息可包括图像获取设备的内参信息和外参信息(即,姿态信息)。
在一些实现方式中,以上映射矩阵为方阵,即,行列数量相等的矩阵,可将其进行分解,例如,可分解为如下公式(1-2)的形式:
其中,h为预设特征点之间的固定距离,例如,目标对象的身高;z为图像获取设备的 安装高度;(P
0 P
1 P
2)为图像获取设备的投影矩阵P的前三列,该投影矩阵P可表示为P=K(R|t),其中,R图像获取设备的旋转矩阵,t为图像获取设备中心位置的三维平移向量,K为图像获取设备的内参矩阵,即,投影矩阵P可以表示为图像获取设备的内参矩阵K与经过三维平移向量t平移后的矩阵与旋转矩阵R的乘积。
在一些实现方式中,可对映射矩阵进行优化,以提升内参信息和位姿信息的准确性。在一些实现方式中,可通过预设特征点的坐标进行优化,以获得图像获取设备的准确的参数信息,例如,内参信息和位姿信息。上述步骤S14可包括以下步骤S141至步骤S143,其中:
步骤S141,根据所述映射矩阵以及所述预设特征点,获得所述预设特征点的误差信息;
步骤S142,根据所述误差信息,对所述映射矩阵的参数进行调整,获得调整后的映射矩阵;
步骤S143,根据所述调整后的映射矩阵,获得所述图像获取设备的内参信息和位姿信息。
在一些实现方式中,以头顶和脚底的预设特征点为例,可确定所述预设特征点的误差信息,根据公式(1-1)所描述的映射关系,头顶的预设特征点的坐标可以利用脚底的预设特征点的坐标及映射矩阵的变换获得,然而,由于通过上述方法获得的映射矩阵的初始参数可能存在误差,导致通过脚底的预设特征点的坐标经过映射矩阵的变换后获得的坐标,与头顶的预设特征点的坐标之间存在误差。因此,可通过优化,使该误差缩小,从而使得映射矩阵中的参数的误差缩小,以优化映射矩阵。
在一些实现方式中,可通过以下优化函数(1-3)来进行优化:
f=∑||P
head-P'
head||+∑||P
foot-P'
foot|| (1-3);
其中,P
head为头顶的预设特征点的坐标,P
foot为脚底的预设特征点的坐标,P'
head=H P
foot,即,P'
head为通过映射矩阵对脚底的预设特征点进行变换后获得的坐标;P'
foot=H
-1P
head,即,P'
foot为通过映射矩阵的逆矩阵对头顶的预设特征点进行变换后获得的坐标。如上所述,由于映射矩阵存在误差,使得P
head和P'
head不相等,P
foot和P'
foot也不相等,||P
head-P'
head||为P
head与P'
head之间的二范数,表示二者之间的误差信息,||P
foot-P'
foot||表示P
foot和P'
foot二者之间的二范数,表示二者之间的误差信息。所述误差信息也可通过其他形式来表示,例如,一范数、欧氏距离等,本公开对误差信息的具体形式不做限制。
在一些实现方式中,所有目标对象的上述误差信息之和即为公式(1-3),可使得公式(1-3)的值最小化,以获得使误差信息之和最小的映射矩阵,即可实现对映射矩阵的优化。
在一些实现方式中,可根据上述公式(1-3)的值,调整映射矩阵的参数,例如,可通过梯度下降法等方法来调整映射矩阵的参数使公式(1-3)的值逐步缩小。在多次调整后,公式(1-3)的值不再继续缩小,则可获得调整后的映射矩阵。或者,可通过非线性规划等方式确定公式(1-3)的最小值,从而确定使公式(1-3)的值达到最小值时的映射矩阵(即,调整后的映射矩阵)。本公开对调整方式不做限制。
在一些实现方式中,在调整后的映射矩阵中,根据公式(1-2)可知,基于映射矩阵的参数,按照公式(1-2)的方式对映射矩阵H进行矩阵分解,可获得旋转矩阵、平移向量和内参矩阵。可基于此获得图像获取设备的内参信息和位姿信息。在一些实现方式中,所述内参信息包括所述图像获取设备的焦距,所述位姿信息包括所述图像获取设备的高度、俯仰角、偏航角以及翻滚角。
在一些实现方式中,根据调整后的映射矩阵的参数,基于其分解结果可获得内参矩阵,其内参矩阵的参数可包括图像获取设备的焦距。
在一些实现方式中,可设定图像获取设备的初始参数,例如,图像获取设备所在的位置为所述待处理图像中的原点,其方位角为正北方向,从而可基于映射矩阵的分解获得的旋转矩阵和平移向量,获得图像获取设备的俯仰角、翻滚角和偏航角。本公开对内参信息和位姿信息所包括的参数不做限制。
在一些实现方式中,也可通过上述方式对双肩的预设特征点对应的映射矩阵进行优化,进而求解上述内参信息和位姿信息。还可不进行上述优化步骤,直接使用步骤S13获得的映射矩阵的初始参数求解内参信息和位姿信息,但求解出的参数的误差会高于优化后的参数的误差。本公开对此不做限制。
根据本公开的实施例的标定方法,可通过矩阵的特征分解方式确定预设特征点,然后利用预设特征点来确定映射矩阵,最后根据映射矩阵以及预设特征点确定图像获取设备的内参信息和位姿信息,无需同一目标对象出现在多个预设位置,也无需目标对象配合,即可完成自标定过程,降低了人工工作量和标定成本,可适用于图像获取设备数量多,分布广的场景中,例如,适用于城市监控***的众多相机的自标定中;另外,还可以对映射矩阵进行优化,以减小映射矩阵的误差,提升检测准确性。
图5为本公开实施例提供的标定方法的应用示意图。如图5所示,图像获取设备51为城市的监控***中的任一监控相机,多个人物对象52出现在图像获取设备51的视野范围内, 在对该图像获取设备51进行标定时,可获取图像获取设备51拍摄的视频,并确定其中存在的对象52的待处理图像。
在一些实现方式中,可对待处理图像中的对象进行筛选,以筛选出姿态为站姿,且未受到遮挡的目标对象。例如,可检测各对象的关键点,并基于关键点确定各对象的姿态,以排除非站姿的对象;还可以基于关键点的置信度,确定该关键点所在的位置是否受到遮挡,并排除受到遮挡的对象,从而获得姿态为站姿且未受遮挡的目标对象。
在一些实现方式中,获取各目标对象的预设特征点,例如,表示目标对象身高的预设特征点,即,头顶和脚底的预设特征点。对各目标对象所在区域的掩膜图像的像素点的像素值的协方差矩阵进行特征分解,获得特征向量,其中,较大的特征值对应的特征向量对应的一系列像素点为掩膜图像中的较长的轴线,该轴线与掩膜图像中对目标对象的轮廓的交点即为头顶和脚底的预设特征点。
在一些实现方式中,将目标对象的身高设为固定值,并基于该固定值获得表示各目标对象的映射关系的映射矩阵,如公式(1-1)所示。基于多个目标对象的尺寸关键点的坐标,可求解映射矩阵的初始参数。该初始参数可能存在误差,可对其进行优化,使误差最小化。
在一些实现方式中,通过公式(1-3)对映射矩阵进行优化,获得误差最小化的映射矩阵。进而基于此映射矩阵获得内参信息和位姿信息。例如,根据公式(1-2)对映射矩阵进行分解,得到图像获取设备的内参矩阵、旋转矩阵和平移向量。基于内参矩阵的参数可获得焦距等内参信息,基于旋转矩阵和平移向量可获得俯仰角、偏航角以及翻滚角等姿态信息,基于公式(1-2)的分解结果,可获得图像获取设备的高度等姿态信息。
在一些实现方式中,所述标定方法可用于监控相机数量众多、分布广的城市监控***中的监控相机的自标定,以降低对每个监控相机进行人工标定的工作量。也可用于其他的相机标定场景中,本公开对所述标定方法的适用领域不做限制。
可以理解,本公开提及的上述各个方法实施例,在不违背原理逻辑的情况下,均可以彼此相互结合形成结合后的实施例。本领域技术人员可以理解,在具体实施方式的上述方法中,各步骤的具体执行顺序应当以其功能和可能的内在逻辑确定。
此外,本公开还提供了标定装置、电子设备、计算机可读存储介质、计算机程序产品,均可用来实现本公开提供的任一种标定方法,相应技术方案和描述和参见方法部分的相应记载。
图6为本公开实施例提供的标定装置的框图。如图6所示,所述装置包括:目标对象获取部分61,被配置为对待处理图像进行检测,获取所述待处理图像中的目标对象;特征点确 定部分62,被配置为确定所述目标对象的预设特征点;映射矩阵确定部分63,被配置为根据所述预设特征点,确定与所述预设特征点对应的映射矩阵;参数信息确定部分64,被配置为根据所述映射矩阵以及所述预设特征点,获得图像获取设备的参数信息,所述待处理图像是所述图像获取设备获取的。
在一些实现方式中,所述目标对象获取部分,还被配置为:在所述待处理图像中检测出多个对象;根据所述多个对象的姿态和所述多个对象的遮挡状态中的至少之一,对所述多个对象进行筛选,获得所述目标对象。
在一些实现方式中,所述目标对象获取部分,还被配置为:获取所述多个对象的关键点;根据每个所述对象的关键点,分别确定每个所述对象的姿态;根据每个所述对象的姿态,对所述多个对象进行筛选,获得所述目标对象。
在一些实现方式中,所述目标对象获取部分,还被配置为:获取所述多个对象的关键点;分别确定每个所述对象的关键点的置信度;根据所述关键点的置信度,分别确定每个所述对象的遮挡状态;根据所述遮挡状态,对所述多个对象进行筛选,获得所述目标对象。
在一些实现方式中,所述目标对象获取部分,还被配置为:获得每个所述对象的位置信息;根据每个所述对象的位置信息,获得所述多个对象的关键点。
在一些实现方式中,所述特征点确定部分,还被配置为:获取所述目标对象的掩膜图像;获取所述掩膜图像的多个像素点的协方差矩阵;对所述协方差矩阵进行特征分解,获得特征向量;根据所述特征向量和所述掩膜图像的多个像素点,确定所述预设特征点。
在一些实现方式中,所述参数信息包括内参信息和位姿信息,所述参数信息确定部分,还被配置为:根据所述映射矩阵以及所述预设特征点,获得所述预设特征点的误差信息;根据所述误差信息,对所述映射矩阵的参数进行调整,获得调整后的映射矩阵;根据所述调整后的映射矩阵,获得所述图像获取设备的内参信息和位姿信息。
在一些实现方式中,所述映射矩阵为用于表示同一目标对象的不同预设特征点之间的位置关系的矩阵。
在本公开实施例以及其他的实施例中,“部分”可以是部分电路、部分处理器、部分程序或软件等等,当然也可以是单元,还可以是模块也可以是非模块化的。
在一些实施例中,本公开实施例提供的标定装置具有的功能或包含的模块可以用于执行上文方法实施例描述的方法,其具体实现可以参照上文方法实施例的描述。
本公开实施例还提出一种计算机可读存储介质,其上存储有计算机程序指令,所述计算机程序指令被处理器执行时实现上述方法。计算机可读存储介质可以是非易失性计算机可读 存储介质。
本公开实施例还提出一种电子设备,包括:处理器;用于存储处理器可执行指令的存储器;其中,所述处理器被配置为调用所述存储器存储的指令,以执行上述方法。
本公开实施例还提供了一种计算机程序产品,包括计算机可读代码,当计算机可读代码在设备上运行时,设备中的处理器执行用于实现如上任一实施例提供的标定方法的指令。
本公开实施例还提供了另一种计算机程序产品,用于存储计算机可读指令,指令被执行时使得计算机执行上述任一实施例提供的标定方法的操作。
电子设备可以被提供为终端、服务器或其它形态的设备。
图7为本公开实施例提供的一种电子设备700的框图。例如,电子设备700可以是移动电话,计算机,数字广播终端,消息收发设备,游戏控制台,平板设备,医疗设备,健身设备,个人数字助理等终端。
参照图7,电子设备700可以包括以下一个或多个组件:处理组件702,存储器704,电源组件706,多媒体组件708,音频组件710,输入/输出(I/O)的接口712,传感器组件714,以及通信组件716。
处理组件702通常控制电子设备700的整体操作,诸如与显示,电话呼叫,数据通信,相机操作和记录操作相关联的操作。处理组件702可以包括一个或多个处理器718来执行指令,以完成上述的方法的全部或部分步骤。此外,处理组件702可以包括一个或多个模块,便于处理组件702和其他组件之间的交互。例如,处理组件702可以包括多媒体模块,以方便多媒体组件708和处理组件702之间的交互。
存储器704被配置为存储各种类型的数据以支持在电子设备700的操作。这些数据的示例包括用于在电子设备700上操作的任何应用程序或方法的指令,联系人数据,电话簿数据,消息,图片,视频等。存储器704可以由任何类型的易失性或非易失性存储设备或者它们的组合实现,如静态随机存取存储器(Static Random-Access Memory,SRAM),电可擦除可编程只读存储器(Electrically Erasable Programmableread only memor,EEPROM),可擦除可编程只读存储器(Erasable Programmable Read-Only Memory,EPROM),可编程只读存储器(Programmable Read-only memory,PROM),只读存储器(Read-only memory,ROM),磁存储器,快闪存储器,磁盘或光盘。
电源组件706为电子设备700的各种组件提供电力。电源组件706可以包括电源管理***,一个或多个电源,及其他与为电子设备700生成、管理和分配电力相关联的组件。
多媒体组件708包括在所述电子设备700和用户之间的提供一个输出接口的屏幕。在一 些实施例中,屏幕可以包括液晶显示器(Liquid Crystal Display,LCD)和触摸面板(TouchPanel,TP)。如果屏幕包括触摸面板,屏幕可以被实现为触摸屏,以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边缘,而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中,多媒体组件708包括一个前置摄像头和/或后置摄像头。当电子设备700处于操作模式,如拍摄模式或视频模式时,前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜***或具有焦距和光学变焦能力。
音频组件710被配置为输出和/或输入音频信号。例如,音频组件710包括一个麦克风(microphone,MIC),当电子设备700处于操作模式,如呼叫模式、记录模式和语音识别模式时,麦克风被配置为接收外部音频信号。所接收的音频信号可以被存储在存储器704或经由通信组件716发送。在一些实施例中,音频组件710还包括一个扬声器,用于输出音频信号。
I/O接口712为处理组件702和***接口模块之间提供接口,上述***接口模块可以是键盘,点击轮,按钮等。这些按钮可包括但不限于:主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件714包括一个或多个传感器,用于为电子设备700提供各个方面的状态评估。例如,传感器组件714可以检测到电子设备700的打开/关闭状态,组件的相对定位,例如所述组件为电子设备700的显示器和小键盘,传感器组件714还可以检测电子设备700或电子设备700一个组件的位置改变,用户与电子设备700接触的存在或不存在,电子设备700方位或加速/减速和电子设备700的温度变化。传感器组件714可以包括接近传感器,被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件714还可以包括光传感器,如CMOS或CCD图像传感器,用于在成像应用中使用。在一些实施例中,该传感器组件714还可以包括加速度传感器,陀螺仪传感器,磁传感器,压力传感器或温度传感器。
通信组件716被配置为便于电子设备700和其他设备之间有线或无线方式的通信。电子设备700可以接入基于通信标准的无线网络,如WiFi,2G或3G,或它们的组合。在一些实施例中,通信组件716经由广播信道接收来自外部广播管理***的广播信号或广播相关信息。在一些实施例中,所述通信组件716还包括近场通信(Near Field Communication,NFC)模块,以促进短程通信。例如,在NFC模块可基于射频识别(Radio Frequency Identification,RFID)技术,红外数据协会(Infrared Data Association,IrDA)技术,超宽带(Ultra Wide Band, UWB)技术,蓝牙(bluetooth,BT)技术和其他技术来实现。
在一些实施例中,电子设备700可以被一个或多个应用专用集成电路(Application Specific Integrated Circuit,ASIC)、数字信号处理器(Digital Signal Processing,DSP)、数字信号处理设备(Digital Signal Processing Device,DSPD)、可编程逻辑器件(Programmable Logic Device,PLD)、现场可编程门阵列(Field Programmable Gate Array,FPGA)、控制器、微控制器、微处理器或其他电子元件实现,用于执行上述方法。
在一些实施例中,还提供了一种非易失性计算机可读存储介质,例如包括计算机程序指令的存储器704,上述计算机程序指令可由电子设备700的处理器718执行以完成上述方法。
图8为本公开实施例提供的一种电子设备800的框图。例如,电子设备800可以被提供为一服务器。参照图8,电子设备800包括处理组件802,其还包括一个或多个处理器,以及由存储器804所代表的存储器资源,用于存储可由处理组件802的执行的指令,例如应用程序。存储器804中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外,处理组件802被配置为执行指令,以执行上述方法。
电子设备800还可以包括一个电源组件806被配置为执行电子设备800的电源管理,一个有线或无线网络接口808被配置为将电子设备800连接到网络,和一个输入输出(I/O)接口810。电子设备800可以操作基于存储在存储器804的操作***,例如Windows ServerTM,Mac OS XTM,UnixTM,LinuxTM,FreeBSDTM或类似。
在一些实施例中,还提供了一种非易失性计算机可读存储介质,例如包括计算机程序指令的存储器804,上述计算机程序指令可由电子设备800的处理组件802执行以完成上述方法。其中,该存储介质可以是易失性或非易失的计算机可读取存储介质。
本公开可以是***、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质,其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是但不限于电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括:便携式计算机盘、硬盘、随机存取存储器(Random Access Memory,RAM)、只读存储器(Read-Only Memory,ROM)、可擦式可编程只读存储器(Erasable Programmable Read-Only Memory,EPROM或闪存)、静态随机存取存储器(Static Random Access Memory,SRAM)、便携式压缩盘只读存储器(Compact Disc Read-Only Memory,CD-ROM)、数字多功能盘(Digital Versatile Disc,DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令 的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身,诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如,通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备,或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令,并转发该计算机可读程序指令,以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码,所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++等,以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中,远程计算机可以通过任意种类的网络—包括局域网(Local Area Network,LAN)或广域网(Wide Area Network,WAN)—连接到用户计算机,或者,可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中,通过利用计算机可读程序指令的状态信息来个性化定制电子电路,例如可编程逻辑电路、现场可编程门阵列(Field Programmable Gate Array,FPGA)或可编程逻辑阵列(PLA),该电子电路可以执行计算机可读程序指令,从而实现本公开的各个方面。
这里参照根据本公开实施例的方法、装置(***)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解,流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合,都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器,从而生产出一种机器,使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时,产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中,这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作,从而,存储有指令的计算机可读介质则包括一个制造品,其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各 个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上,使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤,以产生计算机实现的过程,从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本公开的多个实施例的***、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上,流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分,所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中,方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如,两个连续的方框实际上可以基本并行地执行,它们有时也可以按相反的顺序执行,这依所涉及的功能而定。也要注意的是,框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合,可以用执行规定的功能或动作的专用的基于硬件的***来实现,或者可以用专用硬件与计算机指令的组合来实现。
该计算机程序产品可以具体通过硬件、软件或其结合的方式实现。在一个可选实施例中,所述计算机程序产品具体体现为计算机存储介质,在另一个可选实施例中,计算机程序产品具体体现为软件产品,例如软件开发包(Software Development Kit,SDK)等等。
以上已经描述了本公开的各实施例,并非穷尽性的,并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下,对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择,旨在最好地解释各实施例的原理、实际应用或对市场中的技术的改进,或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。
本公开实施例提供了一种标定方法及装置、电子设备、存储介质及计算机程序产品,其中,所述标定方法包括:对待处理图像进行检测,获取所述待处理图像中的目标对象;确定所述目标对象的预设特征点;根据预设特征点,确定与所述预设特征点对应的映射矩阵;根据所述映射矩阵以及所述预设特征点,获得图像获取设备的参数信息,所述待处理图像是所述图像获取设备获取的。根据本公开的实施例的标定方法,可通过图像中任意目标对象所在区域的预设特征点来确定映射矩阵,进而确定图像获取设备的内参信息和位姿信息,无需同一目标对象出现在多个预设位置,也无需目标对象配合,即可完成自标定过程。降低了人工工作量和标定成本,可适用于图像获取设备数量多,分布广的场景中。
Claims (19)
- 一种标定方法,包括:对待处理图像进行检测,获取所述待处理图像中的目标对象;确定所述目标对象的预设特征点;根据所述预设特征点,确定与所述预设特征点对应的映射矩阵;根据所述映射矩阵以及所述预设特征点,获得图像获取设备的参数信息,所述待处理图像是所述图像获取设备获取的。
- 根据权利要求1所述的方法,其中,所述对待处理图像进行检测,获取所述待处理图像中的目标对象,包括:在所述待处理图像中检测出多个对象;根据所述多个对象的姿态和所述多个对象的遮挡状态中的至少之一,对所述多个对象进行筛选,获得所述目标对象。
- 根据权利要求2所述的方法,其中,所述根据所述多个对象的姿态和所述多个对象的遮挡状态中的至少之一,对所述多个对象进行筛选,获得所述目标对象,包括:获取所述多个对象的关键点;根据每个所述对象的关键点,分别确定每个所述对象的姿态;根据每个所述对象的姿态,对所述多个对象进行筛选,获得所述目标对象。
- 根据权利要求2所述的方法,其中,所述根据所述多个对象的姿态和所述多个对象的遮挡状态中的至少之一,对所述多个对象进行筛选,获得所述目标对象,包括:获取所述多个对象的关键点;分别确定每个所述对象的关键点的置信度;根据所述关键点的置信度,分别确定每个所述对象的遮挡状态;根据每个所述对象的遮挡状态,对所述多个对象进行筛选,获得所述目标对象。
- 根据权利要求3或4所述的方法,其中,所述获取所述多个对象的关键点,包括:获得每个所述对象的位置信息;根据每个所述对象的位置信息,获得所述多个对象的关键点。
- 根据权利要求1至5中任一项所述的方法,其中,所述确定所述目标对象的预设特征点,包括:获取所述目标对象的掩膜图像;获取所述掩膜图像的多个像素点的协方差矩阵;对所述协方差矩阵进行特征分解,获得特征向量;根据所述特征向量和所述掩膜图像的多个像素点,确定所述预设特征点。
- 根据权利要求1至6中任一项所述的方法,其中,所述参数信息包括内参信息和位姿信息,所述根据所述映射矩阵以及所述预设特征点,获得图像获取设备的参数信息,包括:根据所述映射矩阵以及所述预设特征点,获得所述预设特征点的误差信息;根据所述误差信息,对所述映射矩阵的参数进行调整,获得调整后的映射矩阵;根据所述调整后的映射矩阵,获得所述图像获取设备的内参信息和位姿信息。
- 根据权利要求1至7中任一项所述的方法,其中,所述映射矩阵为用于表示同一目标对象的不同预设特征点之间的位置关系的矩阵。
- 一种标定装置,包括:目标对象获取部分,被配置为对待处理图像进行检测,获取所述待处理图像中的目标对象;特征点确定部分,被配置为确定所述目标对象的预设特征点;映射矩阵确定部分,被配置为根据所述预设特征点,确定与所述预设特征点对应的映射矩阵;参数信息确定部分,被配置为根据所述映射矩阵以及所述预设特征点,获得图像获取设备的参数信息,所述待处理图像是所述图像获取设备获取的。
- 根据权利要求9所述的装置,其中,所述目标对象获取部分,还被配置为:在所述待处理图像中检测出多个对象;根据所述多个对象的姿态和所述多个对象的遮挡状态中的至少之一,对所述多个对象进行筛选,获得所述目标对象。
- 根据权利要求10所述的装置,其中,所述目标对象获取部分,还被配置为:获取所述多个对象的关键点;根据每个所述对象的关键点,分别确定每个所述对象的姿态;根据每个所述对象的姿态,对所述多个对象进行筛选,获得所述目标对象。
- 根据权利要求10所述的装置,其中,所述目标对象获取部分,还被配置为:获取所述多个对象的关键点;分别确定每个所述对象的关键点的置信度;根据所述关键点的置信度,分别确定每个所述对象的遮挡状态;根据每个所述对象的遮挡状态,对所述多个对象进行筛选,获得所述目标对象。
- 根据权利要求11或12所述的装置,其中,所述目标对象获取部分,还被配置为:获得每个所述对象的位置信息;根据每个所述对象的位置信息,获得所述多个对象的关键点。
- 根据权利要求9至13中任一项所述的装置,其中,所述特征点确定部分,还被配置为:获取所述目标对象的掩膜图像;获取所述掩膜图像的多个像素点的协方差矩阵;对所述协方差矩阵进行特征分解,获得特征向量;根据所述特征向量和所述掩膜图像的多个像素点,确定所述预设特征点。
- 根据权利要求9至14中任一项所述的装置,其中,所述参数信息包括内参信息和位姿信息,所述参数信息确定模块,还被配置为:根据所述映射矩阵以及所述预设特征点,获得所述预设特征点的误差信息;根据所述误差信息,对所述映射矩阵的参数进行调整,获得调整后的映射矩阵;根据所述调整后的映射矩阵,获得所述图像获取设备的内参信息和位姿信息。
- 根据权利要求9至15中任一项所述的装置,其中,所述映射矩阵为用于表示同一目标对象的不同预设特征点之间的位置关系的矩阵。
- 一种电子设备,包括:处理器;用于存储处理器可执行指令的存储器;其中,所述处理器被配置为调用所述存储器存储的指令,以执行权利要求1至8中任意一项所述的方法。
- 一种计算机可读存储介质,其上存储有计算机程序指令,所述计算机程序指令被处理器执行时实现权利要求1至8中任意一项所述的方法。
- 一种计算机程序产品,所述计算机程序产品包括计算机程序或指令,在所述计算机程序或指令在电子设备上运行的情况下,使得所述电子设备执行权利要求1至8中任一项所述的标记方法的步骤。
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CN114170324A (zh) * | 2021-12-09 | 2022-03-11 | 深圳市商汤科技有限公司 | 标定方法及装置、电子设备和存储介质 |
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CN116563949A (zh) * | 2023-07-05 | 2023-08-08 | 四川弘和数智集团有限公司 | 一种行为识别方法、装置、设备及介质 |
CN117078735A (zh) * | 2023-08-14 | 2023-11-17 | 广州广电运通智能科技有限公司 | 身高检测方法、***、电子设备和存储介质 |
CN117218212A (zh) * | 2023-11-09 | 2023-12-12 | 杭州巨岩欣成科技有限公司 | 相机标定自适应调整方法、装置、计算机设备及存储介质 |
CN117218212B (zh) * | 2023-11-09 | 2024-02-13 | 杭州巨岩欣成科技有限公司 | 相机标定自适应调整方法、装置、计算机设备及存储介质 |
CN118191565A (zh) * | 2024-05-15 | 2024-06-14 | 龙南鼎泰电子科技有限公司 | 用于乘用车电路板制造的检测***和检测方法 |
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