CN112528957A - Human motion basic information detection method and system and electronic equipment - Google Patents
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Abstract
The embodiment of the invention provides a human motion basic information detection method, which comprises the following steps: setting a video acquisition component for acquiring images of each frame in the human body movement process; extracting human body key point information from the image of each frame, and filtering the acquired key point information; fusing the key point information subjected to filtering processing; analyzing key point information of human body movement in a period of time to obtain movement information corresponding to the key points, wherein the video acquisition assembly comprises three video acquisition devices arranged on different coordinate axes in the same three-dimensional coordinate system, and the three video acquisition devices are all arranged towards the origin of the coordinate axes. According to the scheme provided by the invention, according to the images acquired by the three video acquisition devices, the key point information is selected from the images for processing, the three-dimensional coordinate information of the key points of the human body is accurately acquired, and the universal basic information of the human body movement is provided for a professional application system.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a human motion basic information system based on skeleton key points.
Background
The extraction of key points of human bones is the basis of a plurality of human motion state intelligent evaluation systems, such as gymnastics evaluation, pull-up detection and the like. In the prior art, multiple corresponding detection algorithms are developed based on specific application scenes, and a general human motion basic system is lacked, namely three-dimensional coordinates of key points of a human body are obtained through the system, a track, speed and acceleration of one key point are obtained according to needs, or any two key points are combined, and one key point is taken as a center to obtain the angular speed of the other key point.
Patent CN110941990A discloses a method and a device for human body motion evaluation based on skeleton key points, in the scheme, human body motion evaluation is realized based on skeleton key points, and the evaluation is based on two-dimensional coordinates, so that a large error exists, and the requirement of an accuracy evaluation system cannot be met. In addition, a scheme for realizing single-step multi-person absolute three-dimensional posture by using a key point matching algorithm based on depth is provided, although the scheme can acquire the three-dimensional posture coordinates of a human body, the visual angle is single, some key points have high probability of being shielded, and the coordinates of the shielded key points are difficult to acquire under the condition. In addition, the two schemes do not realize a universal human motion basic information system.
Therefore, it is required to provide a general human motion basic information detection method that can meet the requirements of an accuracy evaluation system.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, a system, and an electronic device for detecting basic information of human motion, which at least partially solve the problems in the prior art.
In a first aspect, an embodiment of the present invention provides a method for detecting human motion basic information, where the method includes the following steps:
setting a video acquisition component for acquiring images of each frame in the human body movement process;
extracting human body key point information from the image of each frame, and filtering the acquired key point information;
fusing the key point information subjected to filtering processing;
analyzing key point information of human motion in a period of time to obtain motion information corresponding to the key points,
the video acquisition assembly comprises three video acquisition devices arranged on different coordinate axes in the same three-dimensional coordinate system, and the three video acquisition devices are all arranged towards the origin of the coordinate axes.
The human motion basic information detection method provided by the invention further comprises the following characteristics that the key points are key points based on human bones, and the key points are configured as points for feeding back human postures.
The human motion basic information detection method provided by the invention further comprises the following characteristics that the key points comprise the nose, the neck, the right shoulder, the right elbow, the right wrist, the left shoulder, the left elbow, the left wrist, the right hip, the right knee, the right ankle, the left hip, the left knee, the left ankle, the right eye, the left eye, the right ear and the left ear of the human body.
The human motion basic information detection method provided by the invention also comprises the following characteristics that the key point filtering processing comprises the following steps:
judging the usability of the key point data according to whether the collected key point data exceeds a preset accuracy threshold value;
and if the key point data is unavailable, the key point data at the moment is the same as the key point data at the last moment.
The human motion basic information detection method provided by the invention further comprises the following characteristic that the accuracy threshold is the maximum value of normal distribution of the unit time change of the key point.
The human motion basic information detection method provided by the invention also comprises the following characteristics that the fusion steps are as follows:
calibrating the relation between the pixel point of the camera and the actual position;
and attributing the information of the same key point and different coordinates to the same key point to form the three-dimensional coordinate information of the human body key point.
The human motion basic information detection method provided by the invention also comprises the following characteristics that the motion information of the key points comprises the angular speed of the point A relative to the point B within a period of time t, and the analysis steps are as follows:
(1) firstly, recalculating coordinates of the point A at the starting time A1 and the ending time A2 by taking the point B as a central point;
(2) computing vectors
The included angle theta between the two is equal to,
(3) the angular velocity ω, ω ═ θ/t is calculated.
In a second aspect, an embodiment of the present invention provides a human motion basic information detection system, including:
the video acquisition module is used for acquiring a video of a human body motion process;
the key point extraction module is used for extracting key point information from the video acquired by the video acquisition module;
and the data processing module is used for processing the key point information extracted by the key point extraction information to obtain the motion information of the key points.
The human motion basic information detection system provided by the embodiment of the invention also has the following characteristics that the video acquisition module comprises three video acquisition devices arranged on different coordinate axes in the same three-dimensional coordinate system, and the three video acquisition devices are all arranged towards the origin of the coordinate axes.
In a third aspect, an embodiment of the present invention further provides an electronic device for detecting basic information of human motion, including:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of the first aspect as set forth in the preceding description or any implementation of the first aspect.
In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method according to the first aspect or any implementation manner of the first aspect.
Advantageous effects
The human motion basic information detection method provided by the invention comprises the following steps: setting a video acquisition component for acquiring images of each frame in the human body movement process; extracting human body key point information from the image of each frame, and filtering the acquired key point information; fusing the key point information subjected to filtering processing; analyzing key point information of human body movement in a period of time to obtain movement information corresponding to the key points, wherein the video acquisition assembly comprises three video acquisition devices arranged on different coordinate axes in the same three-dimensional coordinate system, and the three video acquisition devices are all arranged towards the origin of the coordinate axes. According to the scheme provided by the invention, according to the images acquired by the three video acquisition devices, the key point information is selected from the images for processing, the three-dimensional coordinate information of the key points of the human body is accurately acquired, and the universal basic information of the human body movement is provided for a professional application system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for detecting basic information of human motion according to an embodiment of the present invention;
fig. 2 is a schematic configuration diagram of a video capture component of a human motion basic information detection method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a Z-axis image extraction key point of a video acquisition component in the human motion basic information detection method according to the embodiment of the present invention;
fig. 4 is a schematic diagram of an X-axis image extraction key point of a video acquisition component in a human motion basic information detection method according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a key point extracted from a Y-axis image of a video capture component in the method for detecting basic information on human motion according to the embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating the calculation of the angular velocity of point A relative to point B according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. 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.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
As shown in fig. 1, an embodiment of the present disclosure provides a method for detecting human motion basic information, including:
s1: setting a video acquisition component for acquiring images of each frame in the human body movement process;
the human motion basic information detection method provided by the embodiment of the disclosure can be applied to intelligent evaluation of human motion states, such as gymnastics evaluation, pull-up detection and other occasions.
As shown in fig. 2, the video capture component includes three video capture devices disposed on different coordinate axes in the same three-dimensional coordinate system, the three video capture devices are all disposed toward the origin of the coordinate axes, each video capture device obtains X, Y, Z coordinate information of the same key point, the video capture component captures each frame image corresponding to different coordinate axes in the coordinate system from three directions, the frame image can be directly sent to the processor for subsequent analysis and processing operations, the captured image can be stored in a preset storage space, and when a specific moment of a detected person needs to be analyzed, a corresponding video can be obtained from the storage space for analysis and processing. The video capture device may be a camera.
S2: extracting human body key point information from the image of each frame, and filtering the acquired key point information;
in the embodiment of the disclosure, the accuracy of the key points extracted based on the openposition and other deep learning algorithms is related to the model generated through learning, and only information of a single image is considered in the training process, and the time relevance implied by continuous images in the video is avoided, so that the key point information acquired by each frame of image may have large leap, and the leap is corrected by adopting a filtering method. The precision of the key points can be further improved by carrying out filtering processing.
Extracting a feature map of the target person from the acquired image of all the target persons executing a preset sports program by using a preset algorithm, for example, a human posture recognition project (Open _ pos), dividing the feature map into two branches, and extracting a Confidence map (Part Confidence Maps) and a partial association field map (Part Affinity Fields, PAF for short) by using a convolutional neural network respectively; after obtaining the two pieces of information, we use even Matching (Bipartite Matching) in graph theory to find Part of associated points (Part Association), connect the joint points of the target personnel, and due to the vector nature of the PAF, the generated even Matching is correct, and finally the even Matching is combined into the target human body key point information set, and certainly, other algorithms can be used to calculate the target human body key point information set.
The method comprises the steps of obtaining a normal distribution of key points changing in unit time (such as each frame time interval) through experiments, taking the maximum value of the distribution as an upper limit value, when the position of a certain key point is detected, firstly obtaining the change value of the key point and the position of the previous moment, and if the change value exceeds the upper limit value, considering the change value as an abnormal value, and keeping the position of the key point unchanged.
S3: fusing the key point information subjected to filtering processing;
in the embodiment of the disclosure, the key points acquired by each video acquisition device are fused to accurately form the three-dimensional coordinates of the key points of the human body. The fusion algorithm basic principle is as follows: firstly, calibrating the relation between an image pixel point acquired by each video acquisition device and an actual position, wherein each video acquisition device can calibrate information of two dimensions, such as an X-axis video acquisition device and Y/Z coordinate information of a pixel point; and then belonging to the same key point according to the same key point and different coordinate information obtained by each video acquisition device, thereby forming the three-dimensional coordinate information of the key point of the human body.
For example, as shown in fig. 3 to 5, for the key points extracted for the Z, X, Y axis images, the coordinate values (X1, Y1)/(Z1, Y2)/(X2, Z2) can be calculated for the key points a, and it can be seen that two coordinate values are obtained for each dimension, and the average value is taken as the fused coordinate value, that is, the fused coordinate value
X＝(X1+X2)/2；
Y＝(Y1+Y2)/2；
Z＝(Z1+Z2)/2。
S4: analyzing key point information of human motion in a period of time to obtain motion information corresponding to the key points,
the embodiment of the invention provides a human motion basic information exchange interface, and the information such as the track, the speed, the acceleration, the angular speed of one point relative to another point and the like of the corresponding key point is analyzed according to the requirement of a user.
For example, as shown in fig. 6, the angular velocity of point a relative to point B over a period of time t is obtained by the following basic steps:
(1) firstly, recalculating coordinates of the point A at the starting time A1 and the ending time A2 by taking the point B as a central point;
(2) computing vectors
The included angle theta between the two is equal to,
(3) the angular velocity ω, ω ═ θ/t is calculated.
On the basis of the above embodiment, the key points are key points based on human bones, and the key points are configured as points for feeding back human postures. The key points comprise the nose, the neck, the right shoulder, the right elbow, the right wrist, the left shoulder, the left elbow, the left wrist, the right hip, the right knee, the right ankle, the left hip, the left knee, the left ankle, the right eye, the left eye, the right ear and the left ear of the human body.
The main movable joints of the human body are set as key points to evaluate the posture and the action of the human body, and the evaluation method is simple, high in efficiency and high in accuracy. And under different requirements of different users, different key points can be selected to compare motion assessment, and the motion trail of the human body is obtained to assess the motion of the human body, such as the conclusion whether the sports item of the person to be tested is qualified or not, whether the rehabilitation training of the person to be tested reaches the standard or not, and the like.
On the basis of the above embodiment, the keypoint filtering process includes: judging the usability of the key point data according to whether the collected key point data exceeds a preset accuracy threshold value; and if the key point data is unavailable, the key point data at the moment is the same as the key point data at the last moment.
On the basis of the above embodiment, the accuracy threshold is the maximum value of the normal distribution of the change of the key point in unit time.
On the basis of the above embodiment, the fusion steps are as follows: calibrating the relation between the pixel point of the camera and the actual position; and attributing the information of the same key point and different coordinates to the same key point to form the three-dimensional coordinate information of the human body key point.
Corresponding to the above method embodiment, an embodiment of the present invention further provides a human motion basic information detection system, including:
the video acquisition module is used for acquiring a video of a human body motion process; the video acquisition module comprises three video acquisition devices arranged on different coordinate axes in the same three-dimensional coordinate system, and the three video acquisition devices are all arranged towards the origin of the coordinate axes;
the key point extraction module is used for extracting key point information from the video acquired by the video acquisition module;
and the data processing module is used for processing the key point information extracted by the key point extraction information to obtain the motion information of the key points.
The device according to the above embodiment may correspondingly perform the method as follows:
s1: setting a video acquisition component for acquiring images of each frame in the human body movement process;
the video acquisition assembly comprises three video acquisition devices arranged on different coordinate axes in the same three-dimensional coordinate system, the three video acquisition devices are all arranged towards the origin of the coordinate axes, each video acquisition device acquires X, Y, Z coordinate information of the same key point, the video acquisition assembly acquires each frame of image corresponding to the different coordinate axes in the coordinate system from three directions, the image can be directly sent to the processor for subsequent analysis and processing operation, the acquired image can be stored in a preset storage space, and when the specific time of a detected person needs to be analyzed, the corresponding video can be acquired from the storage space for analysis and processing.
S2: extracting human body key point information from the image of each frame, and filtering the acquired key point information;
s3: fusing the key point information subjected to filtering processing;
and fusing the key points acquired by each video acquisition device to accurately form the three-dimensional coordinates of the key points of the human body. The fusion algorithm basic principle is as follows: firstly, calibrating the relation between an image pixel point acquired by each video acquisition device and an actual position, wherein each video acquisition device can calibrate information of two dimensions, such as an X-axis video acquisition device and Y/Z coordinate information of a pixel point; and then belonging to the same key point according to the same key point and different coordinate information obtained by each video acquisition device, thereby forming the three-dimensional coordinate information of the key point of the human body.
S4: analyzing key point information of human motion in a period of time to obtain motion information corresponding to the key points
The embodiment of the invention also provides an electronic device for detecting the basic information of human motion, which comprises:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, enable the at least one processor to perform the human motion basic information detection method in the foregoing method embodiments.
An electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, the ROM, and the RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
Generally, the following devices may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and the like; output devices including, for example, Liquid Crystal Displays (LCDs), speakers, vibrators, and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While the above embodiments describe an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. The computer program, when executed by a processing device, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor, and the human motion basic information detection method in the foregoing method embodiments is provided.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the human motion basic information detection method in the aforementioned method embodiments.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the steps associated with the method embodiments.
Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, enable the electronic device to perform the steps associated with the method embodiments.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system provided by the embodiment, the description is relatively simple because the system corresponds to the method provided by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A human motion basic information detection method is characterized by comprising the following steps:
setting a video acquisition component for acquiring images of each frame in the human body movement process;
extracting human body key point information from the image of each frame, and filtering the acquired key point information;
fusing the key point information subjected to filtering processing;
analyzing key point information of human motion in a period of time to obtain motion information corresponding to the key points,
the video acquisition assembly comprises three video acquisition devices arranged on different coordinate axes in the same three-dimensional coordinate system, and the three video acquisition devices are all arranged towards the origin of the coordinate axes.
2. The human motion basic information detection method according to claim 1, wherein the key points are key points based on human bones, and the key points are configured as points for feeding back human postures.
3. The method according to claim 2, wherein the key points include a nose, a neck, a right shoulder, a right elbow, a right wrist, a left shoulder, a left elbow, a left wrist, a right hip, a right knee, a right ankle, a left hip, a left knee, a left ankle, a right eye, a left eye, a right ear, and a left ear of the human body.
4. The method according to claim 1, wherein the filtering processing of the key points comprises:
judging the usability of the key point data according to whether the collected key point data exceeds a preset accuracy threshold value;
and if the key point data is unavailable, the key point data at the moment is the same as the key point data at the last moment.
5. The human motion basic information detection method according to claim 4, wherein the accuracy threshold is a maximum value of a normal distribution of the time variation of the key points.
6. The method for detecting human motion basic information according to claim 1, wherein the fusing comprises:
calibrating the relation between the pixel point of the camera and the actual position;
and attributing the information of the same key point and different coordinates to the same key point to form the three-dimensional coordinate information of the human body key point.
7. A human motion basic information detection system is characterized by comprising:
the video acquisition module is used for acquiring a video of a human body motion process;
the key point extraction module is used for extracting key point information from the video acquired by the video acquisition module;
and the data processing module is used for processing the key point information extracted by the key point extraction information to obtain the motion information of the key points.
8. The system for detecting the basic information of human motion according to claim 7, wherein the video capture module comprises three video capture devices disposed on different coordinate axes in the same three-dimensional coordinate system, and the three video capture devices are disposed toward the origin of the coordinate axes.
9. An electronic device for detecting basic information of human motion, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the method of any one of claims 1-6.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113229807A (en) * | 2021-05-17 | 2021-08-10 | 四川大学华西医院 | Human body rehabilitation evaluation device, method, electronic device and storage medium |
| CN113255624A (en) * | 2021-07-14 | 2021-08-13 | 北京壹体科技有限公司 | System and method for intelligently identifying completion condition of pull-up action gesture |
| CN113469113A (en) * | 2021-07-19 | 2021-10-01 | 浙江大华技术股份有限公司 | Action counting method and device, electronic equipment and storage medium |
| CN113850248A (en) * | 2021-12-01 | 2021-12-28 | 中科海微(北京)科技有限公司 | Motion attitude evaluation method and device, edge calculation server and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3531342A2 (en) * | 2018-07-04 | 2019-08-28 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method, apparatus and system for human body tracking processing |
| CN110458895A (en) * | 2019-07-31 | 2019-11-15 | 腾讯科技（深圳）有限公司 | Conversion method, device, equipment and the storage medium of image coordinate system |
| CN110738192A (en) * | 2019-10-29 | 2020-01-31 | 腾讯科技（深圳）有限公司 | Human motion function auxiliary evaluation method, device, equipment, system and medium |
| CN111914598A (en) * | 2019-05-09 | 2020-11-10 | 北京四维图新科技股份有限公司 | Method, device and equipment for detecting key points of continuous frame human face and storage medium |
| CN111951326A (en) * | 2019-05-15 | 2020-11-17 | 北京地平线机器人技术研发有限公司 | Target object skeleton key point positioning method and device based on multiple camera devices |
-
2020
- 2020-12-28 CN CN202011576651.7A patent/CN112528957A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3531342A2 (en) * | 2018-07-04 | 2019-08-28 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method, apparatus and system for human body tracking processing |
| CN111914598A (en) * | 2019-05-09 | 2020-11-10 | 北京四维图新科技股份有限公司 | Method, device and equipment for detecting key points of continuous frame human face and storage medium |
| CN111951326A (en) * | 2019-05-15 | 2020-11-17 | 北京地平线机器人技术研发有限公司 | Target object skeleton key point positioning method and device based on multiple camera devices |
| CN110458895A (en) * | 2019-07-31 | 2019-11-15 | 腾讯科技（深圳）有限公司 | Conversion method, device, equipment and the storage medium of image coordinate system |
| CN110738192A (en) * | 2019-10-29 | 2020-01-31 | 腾讯科技（深圳）有限公司 | Human motion function auxiliary evaluation method, device, equipment, system and medium |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113229807A (en) * | 2021-05-17 | 2021-08-10 | 四川大学华西医院 | Human body rehabilitation evaluation device, method, electronic device and storage medium |
| CN113255624A (en) * | 2021-07-14 | 2021-08-13 | 北京壹体科技有限公司 | System and method for intelligently identifying completion condition of pull-up action gesture |
| CN113255624B (en) * | 2021-07-14 | 2021-09-21 | 北京壹体科技有限公司 | System and method for intelligently identifying completion condition of pull-up action gesture |
| CN113469113A (en) * | 2021-07-19 | 2021-10-01 | 浙江大华技术股份有限公司 | Action counting method and device, electronic equipment and storage medium |
| CN113850248A (en) * | 2021-12-01 | 2021-12-28 | 中科海微(北京)科技有限公司 | Motion attitude evaluation method and device, edge calculation server and storage medium |
| CN113850248B (en) * | 2021-12-01 | 2022-02-22 | 中科海微(北京)科技有限公司 | Motion attitude evaluation method and device, edge calculation server and storage medium |
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