CN115047442A - Point cloud data processing method and device, electronic equipment and storage medium - Google Patents
Point cloud data processing method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application relates to a point cloud data processing method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps that millimeter waves are emitted to a target area by a plurality of micro-sensors to obtain point cloud data of the target area, wherein the micro-sensors are used for monitoring different sub-areas in the target area, and the point cloud data are used for representing all objects reflecting the millimeter waves in the target area; and separating target data from the point cloud data, wherein the target data is used for representing the three-dimensional contour of the target object in the target area. The application solves the technical problem of low detection accuracy caused by hardware limitation of the household micro sensor.
Description
Technical Field
The application relates to the technical field of smart home, in particular to a point cloud data processing method and device, electronic equipment and a storage medium.
Background
With the increasing maturity of millimeter wave radar technology in the field of human body security inspection, the application of millimeter waves for human body posture recognition in the market is also in rapid development. In the field of intelligent home furnishing, a micro sensor is adopted to monitor the movement of a human body in the correlation technique, so that the number of the human body and whether the human body falls down are monitored, and an alarm is given or linkage is carried out with other intelligent products. The microsensor contains a millimeter wave radar sensor and can be used for detecting objects and providing information such as distances and angles of the objects. The microsensor is used as a household product, uses lower frequency to transmit millimeter waves, has smaller volume, does not have more components and parts, does not have huge volume, and has lower manufacturing cost.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The application provides a point cloud data processing method and device, electronic equipment and a storage medium, which are used for at least solving the technical problem of low detection accuracy caused by hardware limitation of a household micro-sensor in the related art.
According to an aspect of an embodiment of the present application, there is provided a method for processing point cloud data, including: the method comprises the steps that millimeter waves are emitted to a target area by a plurality of micro-sensors to obtain point cloud data of the target area, wherein the micro-sensors are used for monitoring different sub-areas in the target area, and the point cloud data are used for representing all objects reflecting the millimeter waves in the target area; and separating target data from the point cloud data, wherein the target data is used for representing the three-dimensional contour of the target object in the target area.
According to another aspect of the embodiments of the present application, there is also provided a processing apparatus for point cloud data, including: the acquisition module is used for transmitting millimeter waves to a target area by using a plurality of micro sensors to obtain point cloud data of the target area, wherein the micro sensors are used for monitoring different sub-areas in the target area, and the point cloud data are used for representing all objects reflecting the millimeter waves in the target area; and the processing module is used for separating target data from the point cloud data, wherein the target data is used for representing the three-dimensional contour of the target object in the target area.
According to another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program which, when executed, performs the above-described method.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method described above through the computer program.
According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the steps of any of the embodiments of the method described above.
In the embodiment of the application, millimeter waves are emitted to a target area by using a plurality of micro sensors to obtain point cloud data of the target area, wherein the plurality of micro sensors are used for monitoring different sub-areas in the target area, and the point cloud data is used for representing all objects reflecting the millimeter waves in the target area; the method comprises the steps of separating target data from point cloud data, wherein the target data are used for representing the three-dimensional outline of a target object in a target area, emitting millimeter waves through a plurality of micro sensors at the same time, expanding the detection range, processing the point cloud data acquired by the micro sensors to obtain the three-dimensional outline of the target object, more micro sensors are used for collecting more data of the target area, and more information can be acquired through processing the point cloud data, so that the obtained three-dimensional outline of the target object is more accurate and complete, the detection accuracy of the target area is improved, and the technical problem of lower detection accuracy caused by hardware limitation of household micro sensors is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of a hardware environment for a method of processing point cloud data according to an embodiment of the present application;
FIG. 2 is a flow chart of an alternative method of processing point cloud data according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an alternative micro-sensor monitoring process according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an alternative millimeter wave reflection signal according to an embodiment of the present application;
fig. 5 is a schematic diagram of an alternative user-side interface according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an alternative apparatus for processing point cloud data according to an embodiment of the present application; and the number of the first and second groups,
fig. 7 is a block diagram of a terminal according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
First, partial nouns or terms appearing in the description of the embodiments of the present application are applicable to the following explanations:
compared with a high-precision millimeter wave radar product in the security field, the micro sensor has the advantages of less quantity of transmitting antennas and collecting antennas, lower millimeter wave transmitting frequency and lower manufacturing cost, and can be widely applied to the field of smart homes.
According to an aspect of the embodiments of the present application, an embodiment of a method for processing point cloud data is provided.
Alternatively, in the present embodiment, the above-described processing method of point cloud data may be applied to a hardware environment constituted by the terminal 101 and the server 103 as shown in fig. 1. As shown in fig. 1, a server 103 is connected to a terminal 101 through a network, which may be used to provide a processing service of point cloud data for the terminal or a client installed on the terminal, and a database 105 may be provided on the server or separately from the server, and is used to provide a data storage service for the server 103, where the network includes but is not limited to: the terminal 101 is not limited to a PC, a mobile phone, a tablet computer, an intelligent home control device, and the like. The point cloud data processing method according to the embodiment of the present application may be executed by the server 103, the terminal 101, or both the server 103 and the terminal 101. The terminal 101 may execute the method for processing point cloud data according to the embodiment of the present application by a client installed thereon. The following description will be given by taking an example of a method for processing point cloud data in the embodiment of the present application executed on a server.
Fig. 2 is a flowchart of an alternative method for processing point cloud data according to an embodiment of the present application, and as shown in fig. 2, the method may include the following steps:
step S202, emitting millimeter waves to a target area by using a plurality of micro sensors to obtain point cloud data of the target area, wherein the plurality of micro sensors are used for monitoring different sub-areas in the target area, and the point cloud data are used for representing all objects reflecting the millimeter waves in the target area;
step S204, separating target data from the point cloud data, wherein the target data is used for representing a three-dimensional contour of a target object in a target area.
Through the steps S202 to S204, millimeter waves are emitted by the multiple micro sensors at the same time, the detection range is expanded, the point cloud data acquired by the multiple micro sensors are processed to obtain the three-dimensional profile of the target object, the more the micro sensors are, the more the data collected by the target area are, and the richer information can be acquired through the processing of the point cloud data, so that the obtained three-dimensional profile of the target object is more accurate and complete, the detection accuracy of the target area is improved, and the technical problem of lower detection accuracy caused by the hardware limitation of the household micro sensors is solved.
In the technical scheme provided in step S202, the server utilizes a plurality of micro sensors to emit millimeter waves to the target area to obtain point cloud data of the target area, wherein the plurality of micro sensors are used for monitoring different sub-areas in the target area, and the point cloud data is used for representing all objects reflecting the millimeter waves in the target area.
The micro sensor is a household product containing a millimeter wave radar sensor, and can be used for detecting objects and providing information such as distances and angles of the objects.
Optionally, the multiple micro sensors may be deployed at different positions in a room, or may be deployed in different directions at the same position (each micro sensor transmits a millimeter wave signal at a different transmission angle), so that the millimeter wave signal coverage ranges of the respective micro sensors are not completely overlapped to obtain more indoor information.
Optionally, a number of microsensors or a microsensor and server are connected in some manner to enable communication. For example, a plurality of micro sensors are cascaded, and the cascade connection mode may be various, such as port, wired, wireless, etc., as long as the communication with the plurality of micro sensors is possible. For example, a bluetooth Mesh network is established between a plurality of micro sensors by using bluetooth Mesh technology, and the bluetooth Mesh network can be used for establishing communication between a server and each micro sensor, and even if one micro sensor is disconnected, the connection between other micro sensors is not influenced.
For example, a particular micro-sensor has 7 antennas (3 transmit antennas, 4 collecting antennas), and uses 60Hz millimeter waves, with a 120 degree transmission region. Reflection points obtained after the micro sensors emit millimeter waves are scattered, dozens of reflection points can be obtained from a human body, information in data is less, the approximate outline of the human body cannot be obtained, the three micro sensors are subjected to self-networking through Bluetooth mesh to realize cascade connection, the three cascaded micro sensors are provided with 21 antennas, millimeter wave signals can be emitted at 360 degrees, and a large amount of reflection point data can be obtained.
As an alternative embodiment, the server processes the reflection signal received by each micro-sensor as follows: acquiring a reflection signal received by the micro sensor, wherein the reflection signal is used for representing the coordinate of a reflection point in a first coordinate system, and the first coordinate system takes the micro sensor as the origin of coordinates; and converting the reflected signals into coordinates in a second coordinate system to obtain data of the microsensor in the point cloud data, wherein the second coordinate system takes a reference coordinate point as a coordinate origin, and the reference coordinate point is different from the coordinate origin of the first coordinate system. Through the processing, the reflection signals received by each micro sensor are unified into a second coordinate system to obtain all point cloud data, and the second coordinate system can be a three-dimensional coordinate system established according to a three-dimensional stereogram of the target area.
In the technical solution provided in step S204, the server separates target data from the point cloud data, where the target data is used to represent a three-dimensional contour of a target object in a target area.
As an optional embodiment, the server analyzes the point cloud data through an algorithm model to separate target data of each target object in the point cloud data, for example, the server identifies data of a human body in the point cloud data through a 3D point cloud target detection algorithm, and converges the point cloud data of the same human body to form an approximate three-dimensional contour of the human body.
As an optional embodiment, the server performs clustering processing on the point cloud data to obtain a plurality of groups of clustering data, wherein each group of clustering data is used for representing a three-dimensional contour of an object in a target area; and extracting data of a target category from the multiple groups of clustered data to obtain target data, wherein the target object is an object of the target category.
Optionally, the server processes each group of cluster data as follows: acquiring a three-dimensional contour of an object represented by clustering data; in the case where the three-dimensional contour of the object represented by the cluster data matches the three-dimensional contour of the object of the target category, the cluster data is treated as a set of target data.
The target category may be determined according to actual requirements, for example, if a pet dog in a room is to be monitored, the pet dog is used as the target object, the target category is a dog category or a pet category, and if a person in the room is to be monitored, a human body is used as the target object, and the target category is a human body category.
Optionally, in a case that the object of the target category is a human body, the server acquires three-dimensional contour features of the human body, wherein the three-dimensional contour features of the human body include a head feature, a trunk feature, and a limb feature; comparing the three-dimensional contour of the object represented by the clustering data with the head characteristic, the trunk characteristic and the limb characteristic to obtain a comparison result, wherein the comparison result is the overall similarity between the three-dimensional contour of the object represented by the clustering data and the three-dimensional contour of the human body; and under the condition that the overall similarity is not less than the preset similarity, determining that the three-dimensional contour of the object represented by the clustering data is matched with the three-dimensional contour of the human body, and taking the clustering data as a group of target data. For example, in a case where the overall similarity between the three-dimensional contour of the object represented by the cluster data and the three-dimensional contour of the human body is not less than 90%, the cluster data is regarded as a set of target data, that is, the set of cluster data is point cloud data of the human body in the target region. The above feature comparison process may also use features of other human body three-dimensional contours, for example, features such as multiple key points, intervals between multiple key points, and proportions thereof in the human body three-dimensional contour are used for feature comparison, and the comparison result may also compare relative positions of the features except for the similarity of a single feature, for example, when comparing with head features, trunk features, and limb features, only head, trunk, and upper limbs of an object represented by the cluster data are detected, and the overall similarity may be comprehensively determined by the similarity between the positional relationship between these several features and the positional relationship between these features in the human body contour.
Optionally, the server obtains the number of the target objects in the target area by obtaining the group number of the cluster data matched with the three-dimensional contour of the object of the target category.
Optionally, the server sends the number of the target objects in the target area to other intelligent device control terminals to realize control over the intelligent devices. For example, the number of groups of the cluster data matched with the three-dimensional contour of the human body is 3, that is, 3 persons exist in the current target area, and the server sends the data to the air-conditioning control terminal (a smart sound box, a mobile phone and the like capable of controlling an air conditioner), so that the air-conditioning control terminal can adjust the working state (wind speed, temperature and the like) according to the number of the persons.
As an optional embodiment, the server determines a first plane coordinate of the target object in the target area according to the target data, and stores the first plane coordinate in the database; and fitting the plane coordinates in the database into a motion track of the target object according to the acquisition time, wherein the database further comprises second plane coordinates, the second plane coordinates are determined according to second data of the target object and are in the target area, the second data are data of the target object at a second acquisition time, and the second acquisition time is earlier than the first acquisition time. The motion trail fitting mode can accurately identify the motion trail of the target object moving on the ground in the target area or identify the plane motion trail of the target object moving freely in a three-dimensional space.
Optionally, the server may determine a first spatial coordinate of the target object in the target area according to the target data, and store the first spatial coordinate in the database; and fitting the space coordinates in the database into a motion track of the target object according to the acquisition time, wherein the database further comprises a second space coordinate, the second space coordinate is a space coordinate in the target area determined according to second data of the target object, the second data is data of the target object at a second acquisition time, and the second acquisition time is earlier than the first acquisition time. The first spatial coordinates of the target object within the target region may be coordinates determined from a center of gravity or center of the target object in the target data, and the motion trajectory fitting manner may accurately identify a motion trajectory of the target object that is free to move within a three-dimensional space of the target region.
Optionally, the server may fit a motion trajectory between two acquisition times according to the coordinates of the target object at each acquisition time and the coordinates of the previous acquisition time, and store the motion trajectory fitted each time in the database, where the motion trajectory of the target object at each historical acquisition time is stored in the database.
Optionally, the server sends the motion trajectory of the target object to the user side, the user can check the motion trajectory of the target object on the user side, the server sends the plane coordinate of the target object to the user side, the user can check the ground position of the target object in the target area on the user side, the server sends the space coordinate of the target object to the user side, the user can check the accurate spatial position of the target object in the target area on the user side, the server sends the target data to the user side, the user side forms the three-dimensional contour of the target object at the first acquisition time by using the target data, and the user can check the three-dimensional contour of the target object on the user side.
Optionally, the user end may present the position and the motion trajectory of the target object in the target area in the form of a plane map or a three-dimensional perspective, or may present the position of the target object in the target area and the three-dimensional contour of the target object in the form of a three-dimensional perspective, so that the user can intuitively obtain the current state of the target object, such as the position and the posture (standing, sitting still, lying down, etc.).
Optionally, after the planar coordinates in the database are fitted into the motion trajectory of the target object according to the acquisition time, the server receives a synchronization request, wherein the synchronization request is used for acquiring the motion trajectory and the three-dimensional contour of the target object on the user side in real time; responding to the synchronous request, and sending the first plane coordinates of the target object, the motion trail of the target object and the target data to a user side, wherein the user side is used for: displaying a motion track of the target object on a planar map of the target area in response to a track viewing request of a user; and when a user object viewing request is responded, displaying the three-dimensional outline of the target object, wherein the object viewing request is triggered when a user clicks a preset identifier representing the target object on a user terminal. Through the synchronous operation, the user side can display the motion track of the target object on the plane map in real time, and provide the three-dimensional outline of the target object for the user in real time according to the clicking operation of the user.
Alternatively, when a plurality of target objects exist in the target area, the server may perform synchronous processing on a plurality of target data (corresponding to the plurality of target objects, respectively) by using the processing method for the target data in the above embodiment. For example, the server sends the first plane coordinates, the motion tracks and the target data corresponding to the multiple target objects to the user side, the user side simultaneously displays the preset identifications and the motion tracks of the multiple target objects on the plane map of the target area, and the user can select one of the preset identifications to view so as to obtain the three-dimensional contour of the selected target object.
Optionally, the server may recognize the posture of the target object according to the target data, and send the prompt message to the other device when the posture of the target object is the preset posture. For example, when the server recognizes that the target object (person) falls down according to the target data, the server sends prompt information to the mobile phone of the user to prompt the user that the person falls down in the target area, and the prompt information can also include information such as the first plane coordinate of the target object, the target data and the like, so that the user can check the information on the mobile phone in time.
Optionally, the server sends the positions (the first plane coordinates and the first space coordinates) of the multiple target objects in the target area to the other intelligent device control terminals, so as to control the intelligent device. For example, the server sends the position of each person in the target area to the central air-conditioning control terminal (a smart speaker, a mobile phone, etc. capable of controlling the central air-conditioning), so that the central air-conditioning control terminal adjusts the working state according to the real-time position of each person (adjusts the working state of air outlets in different positions according to the position of the person).
As an alternative example, the following describes the technical solution of the present application in combination with the specific embodiments:
the millimeter wave radar technology has been developed and is mature in the field of human body security inspection, but the AI micro-sensor is used as a household product, and is different from commercial or industrial products, the AI micro-sensor uses 60Hz millimeter waves, has lower frequency and smaller volume, does not have more components and parts or huge volume, cannot achieve the effect of commercial three-dimensional imaging due to the limitation of hardware, and has not high detection accuracy. In the market, the application of millimeter waves for recognizing human body postures is also in rapid development, and the AI micro-sensor monitors the movement of a human body by adopting a millimeter wave radar technology, so that the number of the human body and whether the human body falls down are monitored, and an alarm is given or the AI micro-sensor is linked with other intelligent products. However, the AI microsensor technology has certain limitations, for example, points collected by the AI microsensor are scattered, the number of the points is small, only dozens of points are provided, too few points cannot construct an approximate human body contour, people and pets cannot be accurately distinguished, vital sign signals are weak, people may not monitor the existence of the human body when the people are static for a long time, if the people are close to each other and there is no obvious interval, the AI microsensor cannot be prepared to identify the number of people, and the like, and the AI microsensor is limited in an intelligent home system and does not play a greater role. An AI micro-sensor has 7 antennas (3 transmitting antennas, 4 collecting antennas), the obtained points are scattered, the data information of dozens of points obtained from a human body is less, the approximate outline of the human body cannot be obtained due to the small data amount, and the AI micro-sensor is limited. Fig. 3 is a schematic diagram illustrating an optional micro-sensor monitoring process according to an embodiment of the present disclosure, in which the method is implemented as follows:
1. a plurality of AI microsensor devices (placed at different locations, preferably covering 360 degrees) are placed in the monitoring area to cascade the AI microsensors within the area, either by wire or wirelessly.
2. An AI microsensor has 7 antennas (3 transmitting antennas, 4 collection antennas), and the region of transmitted signal is 120 degrees, and a person can obtain the point comparatively dispersed and less in quantity, as shown in fig. 4, if cascade three AI microsensors, then there are 21 antennas, can 360 degrees transmission millimeter wave signals, can obtain a large amount of intensive points. The more AI microsensor devices are cascaded, the more signals are transmitted and received, and the more data quantity is acquired, the exponential increase.
3. The acquired signals are processed, signals of the same human body are converged to form an approximate human body outline through algorithm model analysis, the human body moves, the position of the outline can be changed, people and pets can be distinguished through the outline, the human body outline is formed instead of a specific image, and therefore privacy of users cannot be invaded, and the method is still suitable for private spaces such as family bedrooms and bathrooms.
4. The profile information is provided for an App (application program) at a user side, the App can show the character track of a user and the posture of the character, and the App can check the posture of the user when the user stands, sits quietly and lies down, as shown in figure 5, the number and the motion track of the user can only be checked in the conventional App, the user cannot acquire the posture and other information, and the character icon is clicked through cascaded equipment to show the posture of the character.
And 5, the cascade connection of the AI micro-sensors can adopt a wireless mode, such as Bluetooth Mesh to carry out ad hoc network, one device is disconnected in time, and the connection among other devices is not influenced.
6. Except can be used for monitoring the old man and tumble in time reporting to the police, child collides with in time notifies the head of a family etc. still can fix a position people's position, links other smart machine, like according to the wind direction of human position automatically regulated air conditioner fan, when the quantity of human in the monitoring area is more, can accurate discernment.
This scheme is through cascading a plurality of AI microsensors, collect more signals to same region body, AI microsensor equipment is more, the signal data that acquires becomes exponential grade and increases, data is abundanter, through the processing to the signal, algorithm model's analysis, acquire abundanter information, thereby can establish approximate human body profile, present more content on the user end, reduce current limitation (detection number is inaccurate when people press close to with the people, long-time static detection is not to the human body, people and the unable accurate discernment of pet etc.). The method has the advantages that the multiple AI microsensors are utilized for cascading, more data are obtained through more dense points, and the AI microsensors can be more intelligent, for example, when multiple people are close to each other, the AI microsensors can accurately identify the number of people instead of mistakenly considering that only one person exists; when the body types of the children and the pets are similar, the children and the pets can be accurately distinguished; when the human body lies, sits for a long time, does not move for a long time, and the physical sign signals are weak, the state of the human body can still be identified, but the human body can not be identified only by moving. The existing small-size AI microsensor is used for acquiring and presenting richer information, so that the small-size AI microsensor is more intelligent in household use.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
According to another aspect of the embodiment of the application, a processing device of point cloud data for implementing the processing method of point cloud data is also provided. Fig. 6 is a schematic diagram of an alternative processing apparatus for point cloud data according to an embodiment of the present application, as shown in fig. 6, the apparatus may include:
the acquisition module 62 is configured to transmit millimeter waves to a target area by using a plurality of micro sensors to obtain point cloud data of the target area, where the plurality of micro sensors are used to monitor different sub-areas in the target area, and the point cloud data is used to represent all objects reflecting the millimeter waves in the target area;
and a processing module 64, configured to separate target data from the point cloud data, where the target data is used to represent a three-dimensional contour of a target object in the target area.
It should be noted that the obtaining module 62 in this embodiment may be configured to execute step S202 in this embodiment, and the processing module 64 in this embodiment may be configured to execute step S204 in this embodiment.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules described above as a part of the apparatus may operate in a hardware environment as shown in fig. 1, and may be implemented by software or hardware.
Through the module, the technical problem of low detection accuracy caused by hardware limitation of the household micro sensor can be solved, and the technical effect of improving the detection accuracy of the target area is achieved.
As an alternative embodiment, the obtaining module 62 is further configured to process the reflection signal received by each of the micro sensors as follows: acquiring a reflection signal received by the micro sensor, wherein the reflection signal is used for representing the coordinate of a reflection point in a first coordinate system, and the first coordinate system takes the micro sensor as the origin of coordinates; and converting the reflected signals into coordinates in a second coordinate system to obtain data of the microsensor in the point cloud data, wherein the second coordinate system takes a reference coordinate point as a coordinate origin, and the reference coordinate point is different from the coordinate origin of the first coordinate system.
As an alternative embodiment, the processing module 64 further includes: the clustering unit is used for clustering the point cloud data to obtain a plurality of groups of clustering data, wherein each group of clustering data is used for representing the three-dimensional contour of an object in the target area; and the extraction unit is used for extracting data of a target category from the multiple groups of clustered data to obtain target data, wherein the target object is an object of the target category.
Optionally, the extracting unit is further configured to process each group of cluster data as follows: acquiring a three-dimensional contour of an object represented by the clustering data; in the case where the three-dimensional contour of the object represented by the cluster data matches the three-dimensional contour of the object of the target category, the cluster data is treated as a set of target data.
Optionally, the extraction unit is further configured to: under the condition that the object of the target category is a human body, acquiring three-dimensional contour features of the human body, wherein the three-dimensional contour features of the human body comprise head features, trunk features and limb features; comparing the three-dimensional contour of the object represented by the clustering data with the head characteristic, the trunk characteristic and the limb characteristic to obtain a comparison result, wherein the comparison result is the overall similarity between the three-dimensional contour of the object represented by the clustering data and the three-dimensional contour of the human body; and under the condition that the overall similarity is not less than the preset similarity, determining that the three-dimensional contour of the object represented by the clustering data is matched with the three-dimensional contour of the human body, and taking the clustering data as a group of target data.
Optionally, the processing module 64 is further configured to: after data of a target category are extracted from the multiple groups of clustered data to obtain target data, determining a first plane coordinate of a target object in a target area according to the target data, and storing the first plane coordinate into a database, wherein the target data are data of the target object at a first acquisition time; and fitting the plane coordinates in the database into a motion track of the target object according to the acquisition time, wherein the database further comprises second plane coordinates, the second plane coordinates are determined according to second data of the target object and are in the target area, the second data are data of the target object at a second acquisition time, and the second acquisition time is earlier than the first acquisition time.
Optionally, the processing module 64 is further configured to: after the plane coordinates in the database are fitted into the motion track of the target object according to the acquisition time, receiving a synchronization request, wherein the synchronization request is used for acquiring the motion track and the three-dimensional contour of the target object on a user terminal in real time; responding to the synchronous request, and sending the first plane coordinates of the target object, the motion trail of the target object and the target data to a user side, wherein the user side is used for: displaying a motion track of the target object on a planar map of the target area in response to a track viewing request of a user; and when a user object viewing request is responded, displaying the three-dimensional outline of the target object, wherein the object viewing request is triggered when a user clicks a preset identifier representing the target object on a user terminal.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules described above as a part of the apparatus may be operated in a hardware environment as shown in fig. 1, and may be implemented by software, or may be implemented by hardware, where the hardware environment includes a network environment.
According to another aspect of the embodiment of the application, a server or a terminal for implementing the above point cloud data processing method is also provided.
Fig. 7 is a block diagram of a terminal according to an embodiment of the present application, and as shown in fig. 7, the terminal may include: one or more processors 701 (only one of which is shown in fig. 7), a memory 703, and a transmission means 705. as shown in fig. 7, the terminal may further include an input-output device 707.
The memory 703 may be used to store software programs and modules, such as program instructions/modules corresponding to the method and apparatus for processing point cloud data in the embodiment of the present application, and the processor 701 executes various functional applications and data processing by running the software programs and modules stored in the memory 703, that is, implements the method for processing point cloud data described above. The memory 703 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, the memory 703 may further include memory located remotely from the processor 701, which may be connected to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 705 is used for receiving or transmitting data via a network, and may also be used for data transmission between a processor and a memory. Examples of the network may include a wired network and a wireless network. In one example, the transmission device 705 includes a Network adapter (NIC) that can be connected to a router via a Network cable and other Network devices to communicate with the internet or a local area Network. In one example, the transmission device 705 is a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
Among other things, the memory 703 is used to store application programs.
The processor 701 may call the application program stored in the memory 703 through the transmission means 705 to perform the following steps: the method comprises the steps that millimeter waves are emitted to a target area by a plurality of micro sensors to obtain point cloud data of the target area, wherein the micro sensors are used for monitoring different sub-areas in the target area, and the point cloud data are used for representing all objects reflecting the millimeter waves in the target area; and separating target data from the point cloud data, wherein the target data is used for representing the three-dimensional contour of the target object in the target area.
The method comprises the steps of emitting millimeter waves to a target area by using a plurality of micro sensors to obtain point cloud data of the target area, wherein the micro sensors are used for monitoring different sub-areas in the target area, and the point cloud data are used for representing all objects reflecting the millimeter waves in the target area; the method comprises the steps of separating target data from point cloud data, wherein the target data are used for representing the three-dimensional outline of a target object in a target area, emitting millimeter waves through a plurality of micro sensors at the same time, enlarging the detection range, processing the point cloud data acquired by the micro sensors to obtain the three-dimensional outline of the target object, the more the micro sensors are, the more data collected on the target area are, and through processing the point cloud data, richer information can be obtained, so that the obtained three-dimensional outline of the target object is more accurate and complete, the detection accuracy of the target area is improved, and the technical problem of lower detection accuracy caused by hardware limitation of household micro sensors is solved.
Optionally, for a specific example in this embodiment, reference may be made to the example described in the foregoing embodiment, and this embodiment is not described herein again.
It can be understood by those skilled in the art that the structure shown in fig. 7 is only an illustration, and the terminal may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, a smart home control device, etc. Fig. 7 does not limit the structure of the electronic device. For example, the terminal may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 7, or have a different configuration than shown in FIG. 7.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
Embodiments of the present application also provide a storage medium. Alternatively, in the present embodiment, the storage medium may be used for a program code for executing a processing method of point cloud data.
Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:
s1, emitting millimeter waves to the target area by using the multiple micro sensors to obtain point cloud data of the target area, wherein the multiple micro sensors are used for monitoring different sub-areas in the target area, and the point cloud data are used for representing all objects reflecting the millimeter waves in the target area;
and S2, separating target data from the point cloud data, wherein the target data is used for representing the three-dimensional contour of the target object in the target area.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method described in the embodiments of the present application.
In the embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be an indirect coupling or communication connection through some interfaces, units or modules, and may be electrical or in other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A method for processing point cloud data is characterized by comprising the following steps:
emitting millimeter waves to a target area by using a plurality of micro sensors to obtain point cloud data of the target area, wherein the micro sensors are used for monitoring different sub-areas in the target area, and the point cloud data is used for representing all objects reflecting the millimeter waves in the target area;
and separating target data from the point cloud data, wherein the target data is used for representing the three-dimensional contour of a target object in the target area.
2. The method of claim 1, wherein separating target data from the point cloud data comprises:
clustering the point cloud data to obtain multiple groups of clustering data, wherein each group of clustering data is used for representing a three-dimensional contour of an object in the target area;
and extracting data of a target category from the multiple groups of clustering data to obtain the target data, wherein the target object is an object of the target category.
3. The method of claim 2, wherein extracting target class data from the plurality of sets of cluster data to obtain the target data comprises processing each set of cluster data as follows:
acquiring a three-dimensional contour of an object represented by the clustering data;
and in the case that the three-dimensional contour of the object represented by the cluster data matches the three-dimensional contour of the object of the target class, treating the cluster data as a group of the target data.
4. The method according to claim 3, wherein the object of the target class is a human body, and in the case that the three-dimensional contour of the object represented by the cluster data matches the three-dimensional contour of the object of the target class, treating the cluster data as a set of the target data comprises:
acquiring three-dimensional contour features of the human body, wherein the three-dimensional contour features of the human body comprise head features, trunk features and limb features;
comparing the three-dimensional contour of the object represented by the clustering data with the head feature, the trunk feature and the limb feature to obtain a comparison result, wherein the comparison result is the overall similarity between the three-dimensional contour of the object represented by the clustering data and the three-dimensional contour of the human body;
and under the condition that the overall similarity is not less than the preset similarity, determining that the three-dimensional contour of the object represented by the clustering data is matched with the three-dimensional contour of the human body, and taking the clustering data as a group of target data.
5. The method of claim 3, wherein the target data is data of the target object at a first acquisition time, and after extracting data of a target class from the plurality of sets of cluster data and obtaining the target data, the method further comprises:
determining a first plane coordinate of the target object in the target area according to the target data, and storing the first plane coordinate to a database;
and fitting the plane coordinates in the database into the motion track of the target object according to the acquisition time, wherein the database further comprises second plane coordinates, the second plane coordinates are determined according to second data of the target object and are in the target area, the second data are data of the target object at a second acquisition time, and the second acquisition time is earlier than the first acquisition time.
6. The method of claim 5, wherein after fitting the planar coordinates in the database to the motion trajectory of the target object at acquisition time instants, the method further comprises:
receiving a synchronization request, wherein the synchronization request is used for acquiring a motion track and a three-dimensional outline of the target object on a user end in real time;
responding to the synchronization request, and sending the first plane coordinates of the target object, the motion trail of the target object, and the target data to the user side, wherein the user side is configured to: displaying a motion track of the target object on a planar map of the target area in response to a track viewing request of a user; and when a user object viewing request is responded, displaying the three-dimensional outline of the target object, wherein the object viewing request is triggered when a user clicks a preset identifier representing the target object on a user terminal.
7. The method of claim 1, wherein using a plurality of micro-sensors to emit millimeter waves to a target area to obtain point cloud data of the target area comprises processing reflected signals received by each of the micro-sensors as follows:
acquiring a reflection signal received by the micro sensor, wherein the reflection signal is used for representing the coordinate of a reflection point in a first coordinate system, and the first coordinate system takes the micro sensor as a coordinate origin;
and converting the reflection signals into coordinates in a second coordinate system to obtain the data of the microsensor in the point cloud data, wherein the second coordinate system takes a reference coordinate point as a coordinate origin, and the reference coordinate point is different from the coordinate origin of the first coordinate system.
8. An apparatus for processing point cloud data, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for transmitting millimeter waves to a target area by using a plurality of micro sensors to obtain point cloud data of the target area, the micro sensors are used for monitoring different sub-areas in the target area, and the point cloud data is used for representing all objects reflecting the millimeter waves in the target area;
and the processing module is used for separating target data from the point cloud data, wherein the target data is used for representing the three-dimensional contour of a target object in the target area.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the steps of the method for processing point cloud data according to any one of claims 1 to 7 by the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of processing point cloud data according to any one of claims 1 to 7.
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