CN111079663A - High-altitude parabolic monitoring method and device, electronic equipment and storage medium - Google Patents
High-altitude parabolic monitoring method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a high-altitude parabolic monitoring method, a high-altitude parabolic monitoring device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring video information of a current monitoring scene, and performing dynamic background modeling on the current monitoring scene through normal distribution to obtain a background image of the monitoring scene; judging whether a foreground image appears in the image information or not according to the background image; when a foreground image appears in the image information, continuously acquiring the motion information of the foreground image, and calculating the motion track of the foreground image according to the motion information; and judging whether the foreground image is a high-altitude parabola or not based on the motion trail of the foreground image. By carrying out background modeling on the current monitoring scene, the background image is separated from the foreground image, whether the foreground image is a high-altitude parabolic object is independently judged, whether the high-altitude parabolic object exists can be judged in real time, and therefore the monitoring effect of the high-altitude parabolic object is improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a high-altitude parabolic monitoring method and device, electronic equipment and a storage medium.
Background
Along with the development of real estate, the floors of newly-built residential areas are higher and higher, and the problem of high-altitude object throwing is more and more prominent. Most install the surveillance camera head in present residential quarter and monitor the condition in the district, when taking place the parabolic incident in high altitude, relevant personnel can call according to the surveillance video who gathers this parabolic incident in high altitude and look over, but the parabolic condition in specific high altitude needs the manual work to look over frame by frame or look over through slowly putting the camera lens, and not only work load is big, still takes place easily to omit, moreover, the parabolic condition in discovery high altitude that can't be timely. Therefore, the existing high-altitude parabolic event has poor monitoring effect.
Disclosure of Invention
The embodiment of the invention provides a high-altitude parabolic monitoring method which can improve the monitoring effect of a high-altitude parabolic event.
In a first aspect, an embodiment of the present invention provides a method for monitoring a high altitude parabola, including:
acquiring video information of a current monitoring scene, and performing dynamic background modeling on the current monitoring scene through normal distribution to obtain a background image of the monitoring scene, wherein the dynamic background modeling is to perform background modeling on each frame of image in the video information;
judging whether a foreground image appears in the video information or not according to the background image;
when a foreground image appears in the video information, continuously acquiring the motion information of the foreground image, and calculating the motion track of the foreground image according to the motion information;
and judging whether the foreground image is a high-altitude parabola or not based on the motion trail of the foreground image.
Optionally, the obtaining video information of a current monitoring scene and performing dynamic background modeling on the current monitoring scene through normal distribution to obtain a background image of the monitoring scene includes:
acquiring continuous frame images in the real-time video information, wherein each pixel point in the continuous frame images corresponds to K normal distributions, K is larger than 1, and the normal distributions comprise a mean parameter, a variance parameter and a weight parameter;
matching the pixel value of each pixel point of the current frame image with the corresponding K normal distributions, and judging whether each pixel point is matched with the normal distribution meeting the preset condition;
if M normally distributed pixel points meeting preset conditions are matched with the pixel values, performing first parameter updating on the M normally distributed pixel points, and keeping the parameters of the other K-M normally distributed pixel points unchanged, wherein M is more than or equal to 1, and M is less than or equal to K;
if pixel values are not matched with pixel points of normal distribution meeting preset conditions, selecting normal distribution with the largest mean value distance from K normal distributions corresponding to the pixel points to perform weight assignment, and performing second parameter updating on the K normal distributions based on the weight assignment, wherein the mean value distance is a difference value between the pixel values of the pixel points and a mean value parameter in the normal distribution;
selecting N normal distributions based on the variance parameter and/or the weight parameter of the normal distributions, and judging whether the corresponding pixel points belong to background pixel points according to the N normal distributions, wherein N is more than or equal to 1, and N is less than or equal to K;
and constructing a frame background of the current frame image based on the background pixel points, and updating the frame background of the current frame image into a background image of the monitoring scene.
Optionally, the determining whether a foreground image appears in the image information according to the background image includes:
matching the pixel value of each pixel point of the current frame image with the corresponding N normal distributions, and judging whether each pixel point is matched with the normal distribution meeting the preset condition;
if the pixels are not matched with the normal distribution meeting the preset condition, judging that the pixels which are not matched with the normal distribution meeting the preset condition are foreground pixels;
and constructing the frame foreground of the current frame image based on the foreground pixel points, and updating the frame foreground of the frame image into the foreground image of the monitoring scene.
Optionally, the determining whether the foreground image is a high-altitude parabola based on the motion trajectory of the foreground image includes:
judging whether the motion track of the foreground image is in accordance with a preset parabolic track or not;
if the motion track of the foreground image accords with the preset parabolic track, judging that the foreground image is a high-altitude parabola;
and if the motion track of the foreground image does not conform to the preset parabolic track, judging that the foreground image is not a high-altitude parabolic object.
Optionally, the determining whether the foreground image is a high-altitude parabola based on the motion trajectory of the foreground image includes:
constructing a plurality of horizontal detection lines in the background image;
judging whether the number of intersection points of the motion trail of the foreground image and the horizontal detection line is greater than a preset threshold value of the number of intersection points;
if the number of the intersection points of the motion trail of the foreground image and the horizontal detection line is larger than a preset threshold value of the number of the intersection points, judging that the foreground image is a high-altitude parabola;
and if the number of the intersection points of the motion trail of the foreground image and the horizontal detection line is smaller than a preset threshold value of the number of the intersection points, judging that the foreground image is not a high-altitude parabola.
Optionally, the method further includes:
and if the foreground image is a high-altitude parabolic object, sending a high-altitude parabolic object prompting alarm to the current monitoring scene and/or a management department.
Optionally, if the foreground image is a high-altitude parabolic object, sending a high-altitude parabolic object prompt alarm to a current monitoring scene and/or a management department, including:
extracting the foreground image;
performing feature recognition on the foreground image to identify the category of the foreground image;
matching corresponding high-altitude parabolic grades according to the categories of the foreground images;
and sending high-altitude parabolic prompt alarms with corresponding levels to the current monitoring scene and/or a management department based on the high-altitude parabolic levels.
In a second aspect, an embodiment of the present invention provides a high altitude parabola monitoring device, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring video information of a current monitoring scene and carrying out dynamic background modeling on the current monitoring scene through normal distribution so as to obtain a background image of the monitoring scene, and the dynamic background modeling is used for carrying out background modeling on each frame of image in the video information;
the first judgment module is used for judging whether a foreground image appears in the video information or not according to the background image;
the second acquisition module is used for continuously acquiring the motion information of the foreground image when the foreground image appears in the video information and calculating the motion track of the foreground image according to the motion information;
and the second judgment module is used for judging whether the foreground image is a high-altitude parabola or not based on the motion trail of the foreground image.
In a third aspect, an embodiment of the present invention provides an electronic device, including: the monitoring method comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps in the monitoring method for the high altitude parabola provided by the embodiment of the invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the steps in the method for monitoring a high altitude parabola provided by the embodiment of the present invention.
In the embodiment of the invention, the real-time video information of the current monitoring scene is continuously acquired, and the dynamic background modeling is carried out on the current monitoring scene according to the real-time video information so as to obtain a background image of the monitoring scene; judging whether a foreground image appears in the image information or not according to the background image; when a foreground image appears in the image information, continuously acquiring the motion information of the foreground image, and calculating the motion track of the foreground image according to the motion information; and judging whether the foreground image is a high-altitude parabola or not based on the motion trail of the foreground image. The background modeling is carried out on the current monitoring scene, so that the background image is separated from the foreground image, whether the foreground image is a high-altitude parabolic object is independently judged, manual judgment is not needed, and whether the high-altitude parabolic object exists can be judged in real time due to the fact that the background image is obtained through dynamic modeling, and therefore the monitoring effect of the high-altitude parabolic object is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for monitoring a high altitude parabola according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for dynamic background modeling provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a high altitude parabolic monitoring device according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of another high altitude parabolic monitoring device provided by an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of another high altitude parabolic monitoring device provided by an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of another high altitude parabolic monitoring device provided by an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of another high altitude parabolic monitoring device provided by an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of another high altitude parabolic monitoring device provided in accordance with an embodiment of the present invention;
fig. 9 is a schematic structural diagram of another high altitude parabolic monitoring device provided by an embodiment of the invention;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart of a method for monitoring a high altitude parabola according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
101. the method comprises the steps of obtaining video information of a current monitoring scene, and carrying out dynamic background modeling on the current monitoring scene through normal distribution to obtain a background image of the monitoring scene.
The current monitoring scene may be a building scene such as a residential building, a commercial building or an office building, which is monitored by the camera. The monitoring range of the camera can be all floors of a building or floors with more than a certain number of floors, for example floors with more than 4 floors, and the monitoring range can be determined as required when the camera is installed, and the shooting angle of the camera is adjusted, so that the camera can monitor the floors in the corresponding range.
The video information mentioned above can be understood as a sequence of consecutive images captured by the camera. The video information may be target video information shot by a camera in real time, target video information shot by the camera at regular time, or target video information uploaded after a user calls the video information shot by the camera.
The above dynamic background modeling refers to establishing different background images according to different current frame images, that is, each frame image corresponds to a background image. The above background image is represented in the continuous image sequence as: in the continuous image sequence, the pixel value of the pixel point as the background image is not changed or the pixel value is changed within a certain range. The dynamic background modeling depends on the correlation of pixel points in a continuous image sequence among different frame images, and can be understood that the change of the pixel value of one pixel point in the continuous image sequence as a background pixel point is subjected to normal distribution, the pixel value of the background pixel point is distributed in a range in the change process, the range is determined by the change mean value of the pixel value of the background pixel point, and the change distribution of the pixel value of the background pixel point can be considered to be on two sides of the change mean value.
Specifically, referring to fig. 2, fig. 2 is a flowchart of a dynamic background modeling method according to an embodiment of the present invention, and as shown in fig. 2, the dynamic background modeling method includes the following steps:
201. successive frame images in the video information are acquired.
The above-mentioned consecutive frame images are consecutive images in time series.
202. And constructing K normal distributions corresponding to each pixel point in the continuous frame images.
And K is greater than 1, and the normal distribution comprises a mean parameter, a variance parameter and a weight parameter.
In this step, K normal distributions corresponding to each pixel point of the first frame image may be initialized to make the K normal distributions be included, where the K normal distributions may be expressed by the following formula:
wherein, the above-mentioned P (x)j) A normal distribution model for expressing the j pixel point, wherein the normal distribution model comprises K normal distributions, x, of the j pixel pointj,tThe pixel value of the j-th pixel point is expressed as described above
A weight parameter representing ith normal distribution of jth pixel point in the tth frame image
The mean parameter of the ith normal distribution of the jth pixel point in the tth frame image is represented
The variance parameter of the ith normal distribution of the jth pixel point in the tth frame image is represented, the η is the density function of the normal distribution, the sigma is the standard deviation, and
thus obtaining the product.
In the process of initializing K normal distributions corresponding to each pixel point of the first frame image, one normal distribution in each pixel point of the first frame image may be initialized, where the initialization may be to assign a mean parameter in the normal distribution to a pixel value of the corresponding pixel point, assign a weight parameter to 1, and assign a variance to 0 at this time, and assign both the mean parameter and the weight parameter of the remaining normal distributions except the normal distribution to 0. For example, one pixel has 5 normal distributions, that is, K is 5, in the 5 normal distributions, the mean parameter and the weight parameter of one normal distribution are selected for assignment, and the mean parameters and the weight parameters of the remaining 4 normal distributions are all assigned to 0. Since each pixel in the first frame image is not dependent on the previous sequence, the normal distribution of each pixel in the first frame image needs to be initialized.
Certainly, in a possible embodiment, all the normal distributions of each pixel point in the first frame image may be randomly assigned by using a random initialization method, and it should be noted that, in the random assignment process, the sum of the assignments of the weight parameters of all the normal distributions needs to be equal to 1.
203. And matching the pixel value of each pixel point of the current frame image with the corresponding K normal distributions.
The current frame image is not the first frame image.
Taking a pixel point j in the current frame image as an example for illustration, assuming that the current frame image is the t-th frame image, it can be understood that, in the previous first frame image to t-1 frame image, the mean and variance of the pixel values corresponding to each pixel point are known, for example, the average and variance are known up to the t-1 frame imageIn K normal distributions of pixel point j, the average parameter is the sum of all pixel values of pixel point j from the first frame image to the t-1 frame image, and then the sum is divided by the data of the frame image, namely the average parameter is divided by t-1, and the average parameter is obtained
The variance parameter of the pixel point j is the value of the pixel corresponding to the pixel point j in the t-1 th frame image minus the mean parameter
Then, the square is calculated to obtain the variance parameter of
Therefore, K normal distributions of the pixel point j in the t-1 frame image can be obtained:
in the current t frame image, if the pixel point j is a background pixel point, the pixel value x of the pixel point jjOne or more of the k normal distributions described above are satisfied. The reason is that in a monitoring scene, the pixel values corresponding to the background pixel points are usually unchanged or slightly changed, that is, the distribution of the pixel values corresponding to the background pixel points can be predicted within a certain pixel value range, and because the pixel values corresponding to the background pixel points are sampled for a long time, the data amount of the pixel values corresponding to the background pixel points is large enough, so that the pixel values corresponding to the background pixel points obey normal distribution, that is, the data are concentrated near the mean parameter and follow the random variable of the normal distribution, the probability of the values near the mean parameter is large, and the probability of the values far from the mean parameter is small. For example, the pixel value x of the pixel point j in the t-th frame imagej,tRegarded as random variable, if the pixel point j is a background pixel point, xj,tIs that
And (4) taking a value recently. Thus, can pass xj,tAnd
to pixel points according to the relationship ofAnd matching the K normal distributions corresponding to the j.
204. And judging whether each pixel point is matched with normal distribution meeting the preset condition.
If there are M normally distributed pixels whose pixel values match the preset condition, the process proceeds to step 205, and if there are pixels whose pixel values do not match the normally distributed pixels which meet the preset condition, the process proceeds to step 206.
The preset condition may be xj,tAnd
satisfies a preset difference threshold, which may be according to xj,t-1Is determined by the standard deviation in the normal distribution of
Thus obtaining the product. Specifically, x can be judgedj,tAnd
whether the difference of (a) is less than a factor times the standard deviation, e.g. determining xj,tAnd
whether the difference of (a) is less than 1.5 times, 2.5 times, etc. of the standard deviation.
If xj,tAnd
if the difference value is smaller than the coefficient multiple of the standard deviation, it indicates that the pixel point j in the t-th frame obeys the normal distribution, i.e. the normal distribution meeting the preset condition is matched. And traversing to judge whether the pixel point is obeyed K normal distributions or not, so as to judge the number of the pixel point j obeyed K normal distributions in the t-th frame. And traversing each pixel point in the t-th frame so as to judge the normal distribution condition matched with each pixel point.
205. And updating the first parameters of the M normal distributions, and keeping the parameters of the rest K-M normal distributions unchanged.
Wherein M is greater than or equal to 1, and M is less than or equal to K.
In this step, M normal distributions satisfying a preset condition are updated, and the first parameter update refers to updating a mean parameter and a variance parameter in the normal distributions, for example, to be performed
Updated to a new mean value
Will be provided with
Updated to a new mean value
The current normal distribution of the pixel point j in the t-th frame can be obtained. And for one pixel point, updating only by M normal distributions meeting preset conditions, and keeping the parameters of the remaining K-M normal distributions unchanged.
206. And selecting the normal distribution with the largest mean distance from the K normal distributions corresponding to the pixel points for weight assignment, and updating the second parameters of the K normal distributions based on the weight assignment.
The mean distance is the difference between the pixel value of the pixel point and the mean parameter in the normal distribution.
In this step, when one pixel point is not matched with any one of the corresponding K normal distributions, x is selectedj,tAnd
the second parameter update is performed on the normal distribution with the largest difference, and the remaining K-1 normal distributions remain unchanged.
The second parameter update refers to updating the weight parameter in the normal distribution, for example, the weight parameter will be updated
Is updated toNew mean value
Specifically, after updating, whether the pixel point is matched with the new normal distribution is judged again. The weight parameters in the normal distribution can be updated by the following formula:
wherein a is the learning rate of the algorithm, and M isi,tFor the updated matching result, if the pixel point can match the new normal distribution after the weight is updated, M isi,tThe value is 1, if the pixel point still can not be matched with the new normal distribution after the weight is updated, M isi,tThe value is 0.
Because the background pixel points are subjected to normal distribution, if the pixel points can be matched with new normal distribution, the pixel points are considered as background points, and if the pixel points cannot be matched with new normal distribution, the pixel points are considered as foreground points. Specifically, it can be known from the above weight parameter updating equation that if the pixel point can be matched with a new normal distribution, the weight parameter in the final normal distribution is increased, and if the pixel point cannot be matched with the new normal distribution, the weight parameter in the final normal distribution is decreased.
207. And selecting N normal distributions based on the variance parameter and/or the weight parameter of the normal distributions, and judging whether the corresponding pixel points belong to the background pixel points according to the N normal distributions.
And N is the ratio of the weight parameter to the variance parameter in the K normal distributions, which is maximally greater than the N normal distributions, wherein N is greater than or equal to 1, and N is less than or equal to K.
The variance parameter represents the degree of dispersion of the data distribution, and the greater the variance, the greater the degree of dispersion, and the smaller the variance, the smaller the degree of dispersion. The smaller the degree of dispersion, the more obvious the features are, the more concentrated the data are in a small range. Therefore, one background pixel point can select the N normal distributions with the minimum variance parameter in the K normal distributions as the best description of the background.
The weight parameters represent the data support degree of each normal distribution, when the background is continuously unchanged, the distribution data corresponding to the background pixel points in the background can be continuously accumulated, the higher the proportion of the supported normal distribution weight points is, the higher the probability of falling into the normal distribution is. Therefore, one background pixel point can select the N normal distributions with the largest weight parameter in the K normal distributions as the best description of the background. It should be noted that, in the K normal distributions corresponding to one pixel point, the sum of the K weight parameters is 1.
As an embodiment of the present invention, the selection may also be performed according to a ratio of the weight parameter to the variance parameter, and one background pixel point may select N normal distributions with the largest ratio of the weight parameter to the variance parameter in the K fragmentation distributions as the best description of the background.
After determining the N normal distributions corresponding to each pixel point, matching each pixel point in the current t-th frame image with the corresponding N normal distributions again, and when at least one normal distribution is matched, indicating that the pixel point is a background pixel point, and going to step 208. If no one normal distribution is matched, it indicates that the pixel is a foreground pixel, and step 209 is performed.
208. And constructing a frame background of the current frame image based on the background pixel points, and updating the frame background of the current frame image into a background image of the monitored scene.
When the pixel point is determined to be the background pixel point of the current frame image, masking can be performed on the background pixel point of the current frame image to distinguish the background pixel point from the foreground part, so that a frame background corresponding to the current frame image is obtained, and the frame background is updated to the corresponding frame image in the video information, so that each frame background image of the monitoring scene is obtained.
In the embodiment of the invention, the background pixel points of the background image are judged through normal distribution, and whether the pixel points are the background pixel points or not can be predicted through the past data distribution of one pixel point, so that the accuracy of dynamic background modeling is improved.
102. And judging whether a foreground image appears in the video information or not according to the background image.
In the step, the pixel value of each pixel point of the current frame image is matched with the corresponding N normal distributions, and whether each pixel point is matched with the normal distribution meeting the preset condition is judged; if the pixel points are not matched with the normal distribution meeting the preset condition, the pixel points are not subjected to the normal distribution of the background pixel points, and then the pixel points which are not matched with the normal distribution meeting the preset condition are judged as foreground pixel points. Whether a foreground image appears in the video information can be judged through the foreground pixel points, specifically, whether the foreground pixel points exist in a frame image of the foreground image can be judged, so that whether the foreground frame image appears in the video information is judged, and whether the foreground image appears in the video information is judged.
209. And constructing a frame foreground of the current frame image based on the foreground pixel points, and updating the frame foreground of the frame image into a foreground image of the monitored scene.
In the step, the pixel value of each pixel point of the current frame image is matched with the corresponding N normal distributions, and whether each pixel point is matched with the normal distribution meeting the preset condition is judged; if the pixels are not matched with the normal distribution meeting the preset condition, judging that the pixels which are not matched with the normal distribution meeting the preset condition are foreground pixels; and constructing a frame foreground of the current frame image based on the foreground pixel points, and updating the frame foreground of the frame image into a foreground image of the monitoring scene. Similarly, when the pixel points are determined to be foreground pixel points, masking can be performed on the foreground pixel points of the current frame image to distinguish from the background portion, so that a frame foreground corresponding to the current frame image is obtained, and the frame foreground is updated to the corresponding frame image in the video information, so that each frame foreground image of the monitored scene is obtained.
103. And when the foreground image appears in the video information, continuously acquiring the motion information of the foreground image, and calculating the motion track of the foreground image according to the motion information.
In this step, when a foreground image appears in the video information, the foreground image may be continuously subjected to motion tracking through a tracking algorithm to obtain displacement data of the foreground image in the background image in a sequence corresponding to the continuous frame images, and a motion trajectory of the foreground image is calculated according to the displacement data. The displacement data refers to displacement data of pixel point coordinates of the foreground image in the frame image.
104. And judging whether the foreground image is a high-altitude parabola or not based on the motion trail of the foreground image.
In this step, the motion trajectory of the foreground image may be compared with a preset parabolic trajectory, and if the motion trajectory of the foreground image conforms to the preset parabolic trajectory, it may be determined that the foreground image is a high altitude parabola. And if the motion track of the foreground image does not accord with the preset parabolic track, judging that the foreground image is not a high-altitude parabola. The preset motion trajectory may be a downward straight line or a parabolic or the like type trajectory.
Optionally, the motion trajectory may be preset based on the position of the camera, for example, when the camera is shooting corresponding to a building to be monitored, the preset motion trajectory may be a downward straight trajectory, a downward left parabolic trajectory, or a downward right parabolic trajectory. When the camera shoots at one side of the building to be monitored, the preset motion track can be a downward straight line track or a characteristic line track deviated to one side, and in this case, only two types of motion tracks can be preset. It should be noted that the building to be monitored may also be referred to as a monitoring scene.
Optionally, in this step, it may also be determined whether the foreground image is a high-altitude parabola or not by constructing a horizontal detection line in the background image. Specifically, a plurality of horizontal detection lines are constructed in a background image; judging whether the number of intersection points of the motion trail of the foreground image and the horizontal detection line is greater than a preset threshold value of the number of intersection points; if the number of intersection points of the motion trail of the foreground image and the horizontal detection line is larger than a preset threshold value of the number of intersection points, judging that the foreground image is a high-altitude parabola; and if the number of the intersection points of the motion trail of the foreground image and the horizontal detection line is smaller than a preset threshold value of the number of the intersection points, judging that the foreground image is not a high-altitude parabola. Furthermore, the horizontal detection line may be constructed according to floors, for example, one horizontal detection line may be constructed according to an upper window edge or a lower window edge outside each floor, and when a high altitude parabola occurs, the occurrence height of the high altitude parabola may be determined according to the number of intersection points of the motion trajectory of the foreground image and the horizontal detection line, that is, which floor performs the high altitude parabola action. In addition, different high-altitude parabolic grades can be set according to the number of intersection points of the horizontal detection lines.
Optionally, after step 104, when the foreground image is determined to be a high-altitude parabola, a warning about the high-altitude parabola may be automatically sent to the current monitoring scene and/or the management department.
The current monitoring scene refers to a scene of a location where the corresponding camera is deployed, such as a residential area a, a residential area B, and a unit C. When the high-altitude object throwing is detected by the camera of the C unit in the residential area A, a danger alarm is sent out at the C unit in the residential area A so as to prompt people nearby the C unit in the residential area A.
The management department may be a property management department or a city management department or other organization with management authority, such as an owner's committee, a garden fair, etc. In a possible implementation manner, in the prompt alarm sent to the management department, video information of the current monitoring scene is also included, the video information includes continuous frame images of high altitude parabola occurrence, and the alarm information can be sent to the management department or a contact terminal of related personnel through various contact ways, such as mail, mobile phone APP or wechat public signal push and the like.
Optionally, when a foreground image is detected, the foreground image may be extracted, and feature recognition may be performed on the foreground image to identify a category to which the foreground image belongs; matching corresponding high-altitude parabolic grades according to the categories of the foreground images; and sending high-altitude parabolic prompt alarms of corresponding levels to the current monitoring scene and/or a management department based on the matched high-altitude parabolic levels. For example, when the foreground image is identified as a paper sheet or a plastic bag, it can be determined that the high-altitude parabolic grade of the foreground image is low, and when the foreground image is identified as a flowerpot or a mobile phone, it can be determined that the high-altitude parabolic grade of the foreground image is high. The high altitude parabolic level described above may be positively correlated with the hazard level. Different high altitude parabolic alert alerts may be set for different high altitude parabolic levels.
In the embodiment of the invention, the real-time video information of the current monitoring scene is continuously acquired, and the dynamic background modeling is carried out on the current monitoring scene according to the real-time video information so as to obtain the background image of the monitoring scene; judging whether a foreground image appears in the image information or not according to the background image; when a foreground image appears in the image information, continuously acquiring the motion information of the foreground image, and calculating the motion track of the foreground image according to the motion information; and judging whether the foreground image is a high-altitude parabola or not based on the motion trail of the foreground image. The background modeling is carried out on the current monitoring scene, so that the background image is separated from the foreground image, whether the foreground image is a high-altitude parabolic object is independently judged, manual judgment is not needed, and whether the high-altitude parabolic object exists can be judged in real time due to the fact that the background image is obtained through dynamic modeling, and therefore the monitoring effect of the high-altitude parabolic object is improved.
It should be noted that the method for monitoring a high altitude parabola provided by the embodiment of the present invention can be applied to devices such as a mobile terminal, a monitor, a computer, and a server that need to monitor a high altitude parabola behavior.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a high altitude parabola monitoring device according to an embodiment of the present invention, and as shown in fig. 3, the device includes:
the first obtaining module 301 is configured to obtain video information of a current monitoring scene, and perform dynamic background modeling on the current monitoring scene through normal distribution to obtain a background image of the monitoring scene, where the dynamic background modeling is to perform background modeling on each frame of image in the video information;
a first determining module 302, configured to determine whether a foreground image appears in the image information according to the background image;
a second obtaining module 303, configured to continuously obtain motion information of a foreground image when the foreground image appears in the video information, and calculate a motion trajectory of the foreground image according to the motion information;
a second determining module 304, configured to determine whether the foreground image is a high-altitude parabola based on the motion trajectory of the foreground image.
Optionally, the first obtaining module 301 and the second obtaining module 302 may be the same obtaining module, and the first obtaining module 301 and the second obtaining module 302 may also be integrated in the same obtaining module.
Optionally, as shown in fig. 4, the first obtaining module 301 includes:
an obtaining unit 3011, configured to obtain continuous frame images in the video information, where each pixel in the continuous frame images corresponds to K normal distributions, K is greater than 1, and the normal distributions include a mean parameter, a variance parameter, and a weight parameter;
the first judging unit 3012 is configured to match a pixel value of each pixel point of the current frame image with the corresponding K normal distributions, and judge whether each pixel point is matched with a normal distribution that meets a preset condition;
a first updating unit 3013, configured to, if there are M normally distributed pixel points whose pixel values match a preset condition, perform first parameter updating on the M normally distributed pixel points, and keep the remaining K-M normally distributed parameters unchanged, where M is greater than or equal to 1 and M is less than or equal to K;
a second updating unit 3014, configured to select, if there is a pixel point whose pixel value is not matched with the normally distributed pixel point that meets a preset condition, a normally distributed pixel point with a largest mean distance from the K normally distributed pixel points to perform weight assignment, and perform second parameter updating on the K normally distributed pixel points based on the weight assignment, where the mean distance is a difference between the pixel value of the pixel point and a mean parameter in the normally distributed pixel point;
a second determining unit 3015, configured to select N normal distributions based on the variance parameter and/or the weight parameter of the normal distributions, and determine whether a corresponding pixel belongs to a background pixel according to the N normal distributions, where N is greater than or equal to 1, and N is less than or equal to K;
the first constructing unit 3016 is configured to construct a frame background of the current frame image based on the background pixel points, and update the frame background of the current frame image into a background image of the monitored scene.
Optionally, as shown in fig. 5, the first determining module 302 includes:
a third judging unit 3021, configured to match a pixel value of each pixel point of the current frame image with the corresponding N normal distributions, and judge whether each pixel point is matched with a normal distribution that meets a preset condition;
a fourth judging unit 3022, configured to judge, if there is normal distribution in which pixel points are not matched to meet a preset condition, that a pixel point in normal distribution in which the matching is not matched to meet the preset condition is a foreground pixel point;
a second constructing unit 3023, configured to construct a frame foreground of the current frame image based on the foreground pixel point, and update the frame foreground of the frame image to a foreground image of the monitored scene.
Optionally, as shown in fig. 6, the second determining module 304 includes:
a fifth determining unit 3041, configured to determine whether a motion trajectory of the foreground image conforms to a preset parabolic trajectory;
a sixth determining unit 3042, configured to determine that the foreground image is a high-altitude parabola if the motion trajectory of the foreground image matches the preset parabola trajectory;
a seventh determining unit 3043, configured to determine that the foreground image is not a high-altitude parabola if the motion trajectory of the foreground image does not conform to the preset parabola trajectory.
Optionally, as shown in fig. 7, the second determining module 304 includes:
a construction unit 3044 for constructing a plurality of horizontal detection lines in the background image;
an eighth determining unit 3045, configured to determine whether the number of intersections between the motion trajectory of the foreground image and the horizontal detection line is greater than a preset threshold of the number of intersections;
a ninth determining unit 3046, configured to determine that the foreground image is a high-altitude parabola if the number of intersections between the motion trajectory of the foreground image and the horizontal detection line is greater than a preset threshold value;
a tenth determining unit 3047, configured to determine that the foreground image is not a high-altitude parabola if the number of intersection points of the motion trajectory of the foreground image and the horizontal detection line is less than a preset threshold value of the number of intersection points.
Optionally, as shown in fig. 8, the apparatus further includes:
and the prompting module 305 is configured to send a high-altitude parabolic prompt alarm to the current monitoring scene and/or management department if the foreground image is a high-altitude parabolic object.
Optionally, as shown in fig. 9, the prompt module 305 includes:
an extracting unit 3051, configured to extract the foreground image;
the identification unit 3052 is configured to perform feature identification on the foreground image to identify a category to which the foreground image belongs;
the matching unit 3053 is configured to match a corresponding high-altitude parabolic grade according to the category to which the foreground image belongs;
and the prompt unit 3054 is configured to send a high-altitude parabolic prompt alarm of a corresponding level to the current monitoring scene and/or the management department based on the high-altitude parabolic level.
It should be noted that the high-altitude parabolic monitoring device provided by the embodiment of the present invention may be applied to a mobile terminal, a monitor, a computer, a server and other devices that need to monitor high-altitude parabolic behavior.
The high-altitude parabolic monitoring device provided by the embodiment of the invention can realize each process realized by the high-altitude parabolic monitoring method in the method embodiment, and can achieve the same beneficial effects. To avoid repetition, further description is omitted here.
Referring to fig. 10, fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 10, including: a memory 1002, a processor 1001 and a computer program stored on said memory 1002 and executable on said processor 1001, wherein:
the processor 1001 is used for calling the computer program stored in the memory 1002, and executes the following steps:
acquiring video information of a current monitoring scene, and performing dynamic background modeling on the current monitoring scene through normal distribution to obtain a background image of the monitoring scene, wherein the dynamic background modeling is to perform background modeling on each frame of image in the video information;
judging whether a foreground image appears in the video information or not according to the background image;
when a foreground image appears in the video information, continuously acquiring the motion information of the foreground image, and calculating the motion track of the foreground image according to the motion information;
and judging whether the foreground image is a high-altitude parabola or not based on the motion trail of the foreground image.
Optionally, the obtaining video information of a current monitoring scene and performing dynamic background modeling on the current monitoring scene through normal distribution by the processor 1001 to obtain a background image of the monitoring scene includes:
acquiring continuous frame images in the video information, wherein each pixel point in the continuous frame images corresponds to K normal distributions, K is larger than 1, and the normal distributions comprise a mean parameter, a variance parameter and a weight parameter;
matching the pixel value of each pixel point of the current frame image with the corresponding K normal distributions, and judging whether each pixel point is matched with the normal distribution meeting the preset condition;
if M normally distributed pixel points meeting preset conditions are matched with the pixel values, performing first parameter updating on the M normally distributed pixel points, and keeping the parameters of the other K-M normally distributed pixel points unchanged, wherein M is more than or equal to 1, and M is less than or equal to K;
if pixel values are not matched with pixel points of normal distribution meeting preset conditions, selecting normal distribution with the largest mean value distance from K normal distributions corresponding to the pixel points to perform weight assignment, and performing second parameter updating on the K normal distributions based on the weight assignment, wherein the mean value distance is a difference value between the pixel values of the pixel points and a mean value parameter in the normal distribution;
selecting N normal distributions based on the variance parameter and/or the weight parameter of the normal distributions, and judging whether the corresponding pixel points belong to background pixel points according to the N normal distributions, wherein N is more than or equal to 1, and N is less than or equal to K;
and constructing a frame background of the current frame image based on the background pixel points, and updating the frame background of the current frame image into a background image of the monitoring scene.
Optionally, the determining, performed by the processor 1001, whether a foreground image appears in the video information according to the background image includes:
matching the pixel value of each pixel point of the current frame image with the corresponding N normal distributions, and judging whether each pixel point is matched with the normal distribution meeting the preset condition;
if the pixels are not matched with the normal distribution meeting the preset condition, judging that the pixels which are not matched with the normal distribution meeting the preset condition are foreground pixels;
and constructing the frame foreground of the current frame image based on the foreground pixel points, and updating the frame foreground of the frame image into the foreground image of the monitoring scene.
Optionally, the determining, by the processor 1001, whether the foreground image is a high-altitude parabola based on the motion trajectory of the foreground image includes:
judging whether the motion track of the foreground image is in accordance with a preset parabolic track or not;
if the motion track of the foreground image accords with the preset parabolic track, judging that the foreground image is a high-altitude parabola;
and if the motion track of the foreground image does not conform to the preset parabolic track, judging that the foreground image is not a high-altitude parabolic object.
Optionally, the determining, by the processor 1001, whether the foreground image is a high-altitude parabola based on the motion trajectory of the foreground image includes:
constructing a plurality of horizontal detection lines in the background image;
judging whether the number of intersection points of the motion trail of the foreground image and the horizontal detection line is greater than a preset threshold value of the number of intersection points;
if the number of the intersection points of the motion trail of the foreground image and the horizontal detection line is larger than a preset threshold value of the number of the intersection points, judging that the foreground image is a high-altitude parabola;
and if the number of the intersection points of the motion trail of the foreground image and the horizontal detection line is smaller than a preset threshold value of the number of the intersection points, judging that the foreground image is not a high-altitude parabola.
Optionally, the processor 1001 further performs the following steps:
and if the foreground image is a high-altitude parabolic object, sending a high-altitude parabolic object prompting alarm to the current monitoring scene and/or a management department.
Optionally, the sending a high-altitude parabolic warning to the current monitoring scene and/or the management department if the foreground image is a high-altitude parabolic object by the processor 1001 includes:
extracting the foreground image;
performing feature recognition on the foreground image to identify the category of the foreground image;
matching corresponding high-altitude parabolic grades according to the categories of the foreground images;
and sending high-altitude parabolic prompt alarms with corresponding levels to the current monitoring scene and/or a management department based on the high-altitude parabolic levels.
It should be noted that the electronic device provided in the embodiment of the present invention may be applied to a mobile terminal, a monitor, a computer, a server, and other devices that need to monitor a high-altitude parabolic behavior.
The electronic device provided by the embodiment of the invention can realize each process realized by the high-altitude parabolic monitoring method in the method embodiment, can achieve the same beneficial effects, and is not repeated here to avoid repetition.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the high altitude parabola monitoring method provided in the embodiment of the present invention, and can achieve the same technical effect, and is not described here again to avoid repetition.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A high altitude parabola monitoring method is characterized by comprising the following steps:
acquiring video information of a current monitoring scene, and performing dynamic background modeling on the current monitoring scene through normal distribution to obtain a background image of the monitoring scene, wherein the dynamic background modeling is to perform background modeling on each frame of image in the video information;
judging whether a foreground image appears in the video information or not according to the background image;
when a foreground image appears in the video information, continuously acquiring the motion information of the foreground image, and calculating the motion track of the foreground image according to the motion information;
and judging whether the foreground image is a high-altitude parabola or not based on the motion trail of the foreground image.
2. The method of claim 1, wherein the obtaining video information of a current monitoring scene and performing dynamic background modeling on the current monitoring scene through normal distribution to obtain a background image of the monitoring scene comprises:
acquiring continuous frame images in the video information, wherein each pixel point in the continuous frame images corresponds to K normal distributions, K is larger than 1, and the normal distributions comprise a mean parameter, a variance parameter and a weight parameter;
matching the pixel value of each pixel point of the current frame image with the corresponding K normal distributions, and judging whether each pixel point is matched with the normal distribution meeting the preset condition;
if M normally distributed pixel points meeting preset conditions are matched with the pixel values, performing first parameter updating on the M normally distributed pixel points, and keeping the parameters of the other K-M normally distributed pixel points unchanged, wherein M is more than or equal to 1, and M is less than or equal to K;
if pixel values are not matched with pixel points of normal distribution meeting preset conditions, selecting normal distribution with the largest mean value distance from K normal distributions corresponding to the pixel points to perform weight assignment, and performing second parameter updating on the K normal distributions based on the weight assignment, wherein the mean value distance is a difference value between the pixel values of the pixel points and a mean value parameter in the normal distribution;
selecting N normal distributions based on the variance parameter and/or the weight parameter of the normal distributions, and judging whether the corresponding pixel points belong to background pixel points according to the N normal distributions, wherein N is more than or equal to 1, and N is less than or equal to K;
and constructing a frame background of the current frame image based on the background pixel points, and updating the frame background of the current frame image into a background image of the monitoring scene.
3. The method of claim 2, wherein said determining whether a foreground image is present in the video information based on the background image comprises:
matching the pixel value of each pixel point of the current frame image with the corresponding N normal distributions, and judging whether each pixel point is matched with the normal distribution meeting the preset condition;
if the pixels are not matched with the normal distribution meeting the preset condition, judging that the pixels which are not matched with the normal distribution meeting the preset condition are foreground pixels;
and constructing the frame foreground of the current frame image based on the foreground pixel points, and updating the frame foreground of the frame image into the foreground image of the monitoring scene.
4. The method of claim 1, wherein the determining whether the foreground image is a high altitude parabola based on the motion trajectory of the foreground image comprises:
judging whether the motion track of the foreground image is in accordance with a preset parabolic track or not;
if the motion track of the foreground image accords with the preset parabolic track, judging that the foreground image is a high-altitude parabola;
and if the motion track of the foreground image does not conform to the preset parabolic track, judging that the foreground image is not a high-altitude parabolic object.
5. The method of claim 1, wherein the determining whether the foreground image is a high altitude parabola based on the motion trajectory of the foreground image comprises:
constructing a plurality of horizontal detection lines in the background image;
judging whether the number of intersection points of the motion trail of the foreground image and the horizontal detection line is greater than a preset threshold value of the number of intersection points;
if the number of the intersection points of the motion trail of the foreground image and the horizontal detection line is larger than a preset threshold value of the number of the intersection points, judging that the foreground image is a high-altitude parabola;
and if the number of the intersection points of the motion trail of the foreground image and the horizontal detection line is smaller than a preset threshold value of the number of the intersection points, judging that the foreground image is not a high-altitude parabola.
6. The method of claim 1, wherein the method further comprises:
and if the foreground image is a high-altitude parabolic object, sending a high-altitude parabolic object prompting alarm to the current monitoring scene and/or a management department.
7. The method as claimed in claim 6, wherein if the foreground image is a high altitude parabola, then sending out a warning of the high altitude parabola to the current monitoring scene and/or management department, comprising:
extracting the foreground image;
performing feature recognition on the foreground image to identify the category of the foreground image;
matching corresponding high-altitude parabolic grades according to the categories of the foreground images;
and sending high-altitude parabolic prompt alarms with corresponding levels to the current monitoring scene and/or a management department based on the high-altitude parabolic levels.
8. A device for monitoring an aerial object, the device comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring video information of a current monitoring scene and carrying out dynamic background modeling on the current monitoring scene through normal distribution so as to obtain a background image of the monitoring scene, and the dynamic background modeling is used for carrying out background modeling on each frame of image in the video information;
the first judging module is used for judging whether a foreground image appears in the image information or not according to the background image;
the second acquisition module is used for continuously acquiring the motion information of the foreground image when the foreground image appears in the video information and calculating the motion track of the foreground image according to the motion information;
and the second judgment module is used for judging whether the foreground image is a high-altitude parabola or not based on the motion trail of the foreground image.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the method of monitoring a high altitude parabola according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the method for monitoring a high altitude parabola according to any one of claims 1 to 7.
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| CN111079663B (en) | 2022-01-11 |
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