CN110910426A - Action process and action trend identification method, storage medium and electronic device - Google Patents
Action process and action trend identification method, storage medium and electronic device Download PDFInfo
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Abstract
The invention discloses an action process and action trend identification method, a storage medium and an electronic device. The invention can accurately identify the action process of the human body and even pre-judge the action trend of the human body in the human body action identification, and can be widely applied to the sports fields of sports, fitness, dancing, sports and the like.
Description
Technical Field
The invention relates to a machine vision identification technology, in particular to an action process and action trend identification method, a storage medium and an electronic device.
Background
In visual recognition, human body information can be recognized, actions and postures of the human body information can be recognized, the static recognition is realized, a coherent action is performed on a human body, the dynamic process is realized, and the trend recognition cannot be realized by only instant human body recognition in order to recognize the coherent action process of the human body.
Disclosure of Invention
The invention aims to provide an action process and action trend identification method, a storage medium and an electronic device, so as to realize identification of action trends.
The invention is realized by the following technical scheme:
an action process identification method includes:
step A: in the process of human body movement, continuous target space depth images containing the human body are obtained in real time;
and B: separating a human body depth image from each target space depth image;
and C: identifying each skeletal joint of the human body from each human body depth image;
step D: detecting the position and the angle of each bone joint of the human body in each human body depth image;
step E: judging the human body action posture in each human body depth image according to the position and the angle of each bone joint of the human body in each human body depth image;
step F: calculating the difference between the human body action postures in the human body depth images of the adjacent front and rear frames;
step G: and judging the motion process of the human body according to the difference between the motion postures of the human body in the human body depth images of the adjacent front and back frames.
Further, in the step a, continuous target space depth images including the human body are acquired through a depth camera.
Further, the difference between the human motion postures in the human depth images of the adjacent front and rear frames includes a position difference and an angle difference of each skeletal joint of the human body in the human depth images of the adjacent front and rear frames.
Further, each skeletal joint of the human body comprises a head, a neck, a trunk, a left shoulder, a left elbow, a left wrist, a right shoulder, a right elbow, a right wrist, a left hip, a left knee, a left heel, a right hip, a right knee, and a right heel.
Further, the step G includes:
judging the change process of the position and the angle of each bone joint of the human body according to the position difference and the angle difference of each bone joint of the human body in the human body depth images of the adjacent front and back frames;
and judging the action process of the human body according to the change process of the position and the angle of each bone joint of the human body.
An action trend identification method comprises the action process identification method, and further comprises the following steps:
step H: judging the change trend of the position and the angle of each bone joint of the human body according to the change process of the position and the angle of each bone joint of the human body;
step I: and judging the action trend of the human body according to the change trend of the positions and the angles of all the bone joints of the human body.
A computer storage medium on which a computer program is stored which, when executed by a processor, implements an action process identification method as described above and/or an action trend identification method as described above.
An electronic device comprising a memory, a processor and a computer program stored in the memory and executable in the processor, the processor implementing the action process identification method as described above and/or the action trend identification method as described above when executing the computer program.
Compared with the prior art, the action process and action trend identification method, the storage medium and the electronic device provided by the invention have the advantages that the continuous target space depth images containing the human body are obtained, the human body depth image data are separated, the positions and the angles of all bone joints of the human body in all the human body depth images are detected, the action posture of the human body in all the human body depth images is judged according to the positions and the angles, finally, the action process of the human body is judged according to the difference between the action postures of the human body in all the adjacent front and back frame human body depth images, and the action trend of the human body can also be judged according to the difference. The invention can accurately identify the action process of the human body and even pre-judge the action trend of the human body in the human body action identification, and can be widely applied to the sports fields of sports, fitness, dancing, sports and the like.
Drawings
FIG. 1 is a schematic general flow chart of a motion recognition and comparison method according to the present invention;
FIG. 2 is a schematic illustration of a human bone joint;
fig. 3 is a flowchart illustrating an embodiment of the motion recognition and comparison method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the following embodiments and the accompanying drawings.
The invention is mainly applied to action recognition. The motion recognition in this document mainly refers to human motion recognition, that is, a human motion image or video is collected by an image collecting device, and a motion posture or motion trend of a human body is recognized from the human motion image or video. The human body action recognition can be widely applied to the fields of motion sensing games, exercise and fitness guidance and the like. As shown in fig. 1, the method for identifying an action process according to an embodiment of the present invention may include the following steps a to G.
Step A: and in the process of human body movement, continuously acquiring a target space depth image containing the human body in real time. In particular, a depth camera (such as a Tof camera), a structured light projector, and the like can be used to obtain continuous depth images of the target space containing the human body. The human body is in the target space, and the depth images of the human body are acquired. The target spatial depth image is a continuous target spatial depth image obtained in real time, so that the human motion posture in each frame of human depth image can be obtained after the analysis processing in the subsequent step B, C, D, E, and calculation data is provided for the subsequent calculation of the difference between the human motion postures in the adjacent front and rear frames of human depth images.
And B: and separating the human body depth image from each target space depth image. The separated human body depth image is used for the analysis and calculation of the subsequent steps.
And C: and identifying each skeletal joint of the human body from each human body depth image. The skeletal joint identification can be realized by adopting a somatosensory algorithm of Microsoft Kinect to analyze and calculate the human body depth image.
Step D: and detecting the position and the angle of each bone joint of the human body in each human body depth image. The positions and angles of all the bone joints can be realized by analyzing and calculating all the bone joints of the human body identified from the human body depth image by adopting the somatosensory algorithm of Microsoft Kinect.
Step E: and judging the human body action posture in each human body depth image according to the position and the angle of each bone joint of the human body in each human body depth image. The motion posture of the human body is determined by the positions and angles of the skeletal joints of the whole body of the human body, so that the motion posture of the human body can be judged according to the positions and angles of the skeletal joints of the human body. Because the number of human body bone joints is large, for the requirement of general motion identification precision, a plurality of purposes can be realized only by accurately identifying main motions of the human body without identifying fine motions of the human body, so that in the process of identifying the human body bone joints, only the positions and the angles of the main bone joints of the human body need to be identified, and the positions and the angles of all the bone joints of the human body do not need to be identified. Accordingly, when determining each skeletal joint of the human body to be recognized, as shown in fig. 2, 15 human skeletal joints such as the head 101, the neck 102, the trunk 103, the left shoulder 104, the left elbow 105, the left wrist 106, the right shoulder 107, the right elbow 108, the right wrist 109, the left hip 110, the left knee 111, the left heel 112, the right hip 113, the right knee 114, and the right heel 115 may be selected and recognized. By recognizing the positions and angles of these 15 skeletal joints, various major movement gestures of the human body can be recognized.
Step F: and calculating the difference between the human motion postures in the human depth images of the adjacent front and rear frames. The difference between the human motion postures in the human depth images of the adjacent front and rear frames comprises the position difference and the angle difference of each bone joint of the human body in the human depth images of the adjacent front and rear frames. In the specific calculation, taking the above-mentioned 15 bone joints as an example, as shown in fig. 3, the 15 bone joints may be subjected to batch calculation of differences, and finally summarized.
The method comprises the steps of determining the motion process of the human body according to the difference between the motion postures of the human body in the human depth images of the adjacent front and rear frames, determining the motion process of the human body according to the position difference and the angle difference of the bone joints of the human body in the human depth images of the adjacent front and rear frames when the steps are executed, determining the motion process of the human body according to the position difference and the angle difference of the bone joints of the human body in the position and angle change process of the bone joints of the human body, determining the change process of the position and the angle difference of the bone joints of the human body between every two adjacent human depth images of the human body according to the position and the angle difference of the bone joints of the human body in the acquired continuous depth image of the acquired continuous depth, determining the change process of the position and the angle relation of the bone joints of the human body between every two adjacent human depth images of the human body, taking the change process of the position and angle relation of the bone joints of the human body as an example of the lifting and dropping process of the human body by taking the lifting and dropping process of the human body as an elbow, and lifting and dropping the human body, and the corresponding wrist position of the wrist can be determined by the human body in the corresponding human body after the motion vector of the wrist can be determined by the angle of the wrist can be determined by the angle of the wrist can be determined by the angle of the wrist of the angle of the wrist can be found in the angle of the wrist can be found in the angle of the wrist found in the angle of the wrist found in the angle of the wrist found in the angle of the wrist found in the wrist found.
Based on the motion process identification method, another embodiment of the invention also provides an action trend identification method. Motion trends refer to predictions of impending motion. The action trend identification method comprises the action process identification method. On the basis of the motion process identification method, the method further comprises the following steps H and I.
Step H: and judging the change trend of the position and the angle of each bone joint of the human body according to the change process of the position and the angle of each bone joint of the human body. For example, if the analysis and calculation of the human depth images of 3 consecutive groups of adjacent frames show that the positions and angles of the left shoulder, the left elbow, the left wrist, the right shoulder, the right elbow and the right wrist of the human body are all moving processes in an upward motion, it can be determined that the left shoulder, the left elbow, the left wrist, the right shoulder, the right elbow and the right wrist of the human body will continue to move upward immediately.
Step I: and judging the action trend of the human body according to the change trend of the positions and the angles of all the bone joints of the human body. Based on the above assumptions, when it is determined that the left shoulder, the left elbow, the left wrist, the right shoulder, the right elbow, and the right wrist of the human body will continue to move upward, it can be determined that the movement of the human body tends to continue to lift the hands.
Embodiments of the present invention also provide a computer storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for identifying an action process and/or the method for identifying an action trend as described above are/is implemented.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable in the processor, and when the processor executes the computer program, the processor implements the action process identification method and/or the action trend identification method as described above.
The above embodiments are only preferred embodiments and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An action process identification method is characterized by comprising the following steps:
step A: in the process of human body movement, continuous target space depth images containing the human body are obtained in real time;
and B: separating a human body depth image from each target space depth image;
and C: identifying each skeletal joint of the human body from each human body depth image;
step D: detecting the position and the angle of each bone joint of the human body in each human body depth image;
step E: judging the human body action posture in each human body depth image according to the position and the angle of each bone joint of the human body in each human body depth image;
step F: calculating the difference between the human body action postures in the human body depth images of the adjacent front and rear frames;
step G: and judging the motion process of the human body according to the difference between the motion postures of the human body in the human body depth images of the adjacent front and back frames.
2. The motion process recognition method according to claim 1, wherein in step a, continuous depth images of a target space containing the human body are obtained by a depth camera.
3. The motion process recognition method according to claim 1, wherein the difference between the motion postures of the human body in the human body depth images of the adjacent front and rear frames includes a position difference and an angle difference of each skeletal joint of the human body in the human body depth images of the adjacent front and rear frames.
4. The course of action recognition method of claim 3, wherein each skeletal joint of the human body comprises a head, a neck, a torso, a left shoulder, a left elbow, a left wrist, a right shoulder, a right elbow, a right wrist, a left hip, a left knee, a left heel, a right hip, a right knee, a right heel.
5. The action process identifying method according to claim 4, wherein the step G includes:
judging the change process of the position and the angle of each bone joint of the human body according to the position difference and the angle difference of each bone joint of the human body in the human body depth images of the adjacent front and back frames;
and judging the action process of the human body according to the change process of the position and the angle of each bone joint of the human body.
6. An action trend recognition method comprising the action process recognition method according to claim 5, further comprising:
step H: judging the change trend of the position and the angle of each bone joint of the human body according to the change process of the position and the angle of each bone joint of the human body;
step I: and judging the action trend of the human body according to the change trend of the positions and the angles of all the bone joints of the human body.
7. A computer storage medium on which a computer program is stored, which, when being executed by a processor, carries out the action procedure recognition method according to any one of claims 1 to 5 and/or the action trend recognition method according to claim 6.
8. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable in the processor, wherein the processor implements the action process identification method according to any one of claims 1 to 5 and/or the action trend identification method according to claim 6 when executing the computer program.
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| CN114612524A (en) * | 2022-05-11 | 2022-06-10 | 西南交通大学 | A Motion Recognition Method Based on RGB-D Camera |
| CN116246350A (en) * | 2023-05-11 | 2023-06-09 | 山东工程职业技术大学 | Motion monitoring method, device, equipment and storage medium based on motion capture |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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Application publication date: 20200324 |