WO2023083030A1 - Procédé de reconnaissance de posture et dispositif associé - Google Patents

Procédé de reconnaissance de posture et dispositif associé Download PDF

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WO2023083030A1
WO2023083030A1 PCT/CN2022/128504 CN2022128504W WO2023083030A1 WO 2023083030 A1 WO2023083030 A1 WO 2023083030A1 CN 2022128504 W CN2022128504 W CN 2022128504W WO 2023083030 A1 WO2023083030 A1 WO 2023083030A1
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image
target
target object
pose
processed
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PCT/CN2022/128504
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Chinese (zh)
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李志豪
张子霄
许松岑
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华为技术有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20132—Image cropping

Definitions

  • This application relates to the technical field of artificial intelligence (AI), in particular to a gesture recognition method and related equipment.
  • AI artificial intelligence
  • Human body motion capture technology is a commonly used technology in the film and television and game industries. This technology can process the input video stream to capture the posture of the human body in each frame of image, so as to obtain the motion information of the human body. Based on this technology, the posture data of human body movement can be extracted from massive videos, which has a wide range of application scenarios.
  • human body motion capture can be realized based on AI technology.
  • the trained neural network can be used to process the area where the human body is located in the image, so as to obtain posture parameters, and the posture parameters can be used to determine the posture of the human body.
  • the neural network will determine the The posture parameters of the human body in the image are consistent, but in the actual environment, once the human body moves, although the postures of the human body in different positions are similar, there must be subtle differences between the postures of the human body. This is the current neural network. If it cannot be identified, the captured posture of the human body is not accurate enough, which in turn affects the accuracy of the motion information of the human body.
  • the embodiment of the present application provides a posture recognition method and related equipment, which can obtain the posture of the target object after processing the image containing the target object, which has high accuracy, and is conducive to improving the accuracy of the acquired target image. Accuracy of exercise information.
  • the first aspect of the embodiments of the present application provides a gesture recognition method, the method comprising:
  • the target image When it is necessary to perform gesture recognition on the target image, the target image can be obtained first.
  • the target image usually presents the target object and the environment in which the target object is located. It can be understood that the goal of gesture recognition is to obtain the pose of the target object in the target image .
  • the target image can be preprocessed. Specifically, the target image may be detected to determine an area where the target object is located in the target image, which may be called a target area, and position information of the target area in the target image may be acquired.
  • the target area and the position information of the target area in the target image can be input to the gesture recognition model, so that the target area of the target image and the target area in the target image can be identified by the gesture recognition model.
  • the position information in the target image is processed to obtain the attitude parameters. Then, the pose of the target object in the target image can be obtained based on the pose parameters.
  • the target area where the target object is located in the target image and the position information of the target area in the target image can be input into the gesture recognition model, so that the target image of the target image can be identified by the gesture recognition model.
  • the position information of the area and the target area in the target image is processed to obtain the pose parameters, so the pose of the target object can be obtained based on the pose parameters.
  • the input of the gesture recognition model not only includes the cropped target area, but also includes the position information of the target area in the target image, so the gesture recognition model not only considers the image information of the target area itself when performing image processing
  • the impact on the posture of the target object also takes into account the influence of the position information of the target area in the target image on the posture of the target object.
  • the factors considered are relatively comprehensive, so the posture of the target object obtained based on this method , has higher accuracy, which is beneficial to improve the accuracy of the acquired motion information of the target image.
  • the location information includes the coordinates of the center point of the target area in the image coordinate system and the size of the target area, and the image coordinate system is constructed based on the target image.
  • a certain vertex of the target image can be used as the origin of the image coordinate system, then, the coordinates of the center point of the target area in the image coordinate system can be determined, as well as the length and width of the target area (which is equivalent to obtaining The size of the target area), this information can be used to indicate the position of the target area in the target image, so that after inputting the position information of the target area into the gesture recognition model, the gesture recognition model will perform image processing on the target area , can effectively take into account the influence of the position information of the target area in the target image on the pose of the target object, so the pose of the target object obtained based on these information has high accuracy.
  • the location information includes coordinates of vertices of the target area in an image coordinate system, and the image coordinate system is constructed based on the target image.
  • a certain vertex of the target image can be used as the origin of the image coordinate system, then the coordinates of all vertices of the target area in the image coordinate system can be determined, and this information can be used to indicate the position of the target area in the target image.
  • the gesture recognition model can effectively take into account the influence of the position information of the target area in the target image on the pose of the target object when processing the image of the target area. Therefore, the posture of the target object obtained based on these information has a high accuracy.
  • the pose recognition model is trained based on the predicted projection result of the predicted pose of the target object on the image to be processed and the real projection result of the real pose of the target object on the image to be processed to obtain .
  • the gesture recognition model is made to perceive the position of the target object in the whole image, and learn the relationship between the position and the pose of the target object. Then, in the application process of the gesture recognition model, the model can accurately capture the pose of the target object from the whole picture based on the position information of the target object in the whole picture.
  • the pose of the target object includes the orientation of the target object in the camera coordinate system (that is, the orientation of the target object relative to the camera) and the body behavior of the target object in the camera coordinate system (that is, the orientation of the target object in the three-dimensional action in space), the origin of the camera coordinate system is the camera that captures the target image.
  • the gesture recognition model can also recognize the difference between the poses of the target object in these multiple images. The difference between objects, that is, the difference between the orientation of the target object relative to the camera, and the difference between the motion of the target object in three-dimensional space.
  • the method further includes: processing the target area of the target image and the position information of the target area in the target image through the gesture recognition model to obtain shape parameters and displacement parameters, and the pose parameters, shape parameters and The displacement parameters are collectively used to obtain the pose of the target object.
  • the output of the gesture recognition model may include gesture parameters, shape parameters and displacement parameters.
  • the attitude parameter is used to indicate the rotation angle of the target object relative to the camera and the angle between each joint of the target object itself.
  • the shape parameter is used to indicate the three-dimensional shape of the target object.
  • the displacement parameter is used to indicate the range occupied by the target object in the target area, and the offset of the target object in the target area (for example, taking the center point of the target area as a reference point, the degree to which the target object shifts to the left or to degree of right offset).
  • the attitude of the target object can be accurately obtained by calculating based on the attitude parameters, shape parameters and displacement parameters.
  • the method further includes: performing normalization processing on the position information of the target area in the target image to obtain the normalized position information; Processing the position information of the target area in the target image to obtain the pose parameters includes: processing the target area of the target image and the normalized position information through a pose recognition model to obtain the pose parameters.
  • the gesture recognition model can be implemented based on the normalized position information when performing the gesture recognition operation. Since the processing of the normalized position information is less difficult, it is beneficial to reduce the calculation amount of the gesture recognition model and reduce The design cost of the model.
  • the target object is a human body.
  • the second aspect of the embodiment of the present application provides a gesture recognition method, the method comprising:
  • the target image When it is necessary to perform gesture recognition on the target image, the target image can be obtained first.
  • the target image usually presents the target object and the environment in which the target object is located. It can be understood that the goal of gesture recognition is to obtain the pose of the target object in the target image .
  • the position information of the pixels in the target image can be obtained, and the target image and the position information of the pixels in the target image can be input into the gesture recognition model, so that the target image and the pixel points in the target image can be detected by the gesture recognition model.
  • the position information is processed to obtain attitude parameters, and the attitude parameters are used to obtain the attitude of the target object included in the target image.
  • the target image and the position information of the pixels in the target image can be input to the gesture recognition model, so as to perform the target image and the position information of the pixels in the target image through the gesture recognition model.
  • the posture parameters are obtained, so the posture of the target object presented by the target image can be obtained based on the posture parameters.
  • the input of the gesture recognition model not only includes the target image, but also includes the position information of the pixels in the target image. Therefore, when the gesture recognition model performs image processing, it not only considers the image information of the target image itself to the pose of the target object.
  • the impact caused by this method also takes into account the influence of the position information of the pixel points in the target image on the attitude of the target object.
  • the factors considered are relatively comprehensive, so the attitude of the target object obtained based on this method has high accuracy. degree, which in turn helps to improve the accuracy of the acquired motion information of the target image.
  • the position information includes the coordinates of the pixel points in the image coordinate system, and the image coordinate system is constructed based on the target image.
  • a certain vertex of the target image can be used as the origin of the image coordinate system, then the coordinates of all pixels in the target image in the image coordinate system can be determined, and this information can be used as the input of the gesture recognition model, so that , the input of the gesture recognition model not only includes each pixel of the target image, but also includes the coordinates of each pixel in the target image in the image coordinate system.
  • the pose recognition model can effectively consider the influence of the position information of all pixels in the target area on the pose of the target object when performing image processing on the target area, Therefore, the posture of the target object obtained based on these information has high accuracy.
  • the pose recognition model is trained based on the predicted projection result of the predicted pose of the target object on the image to be processed and the real projection result of the real pose of the target object on the image to be processed to obtain .
  • the pose of the target object includes the orientation of the target object in the camera coordinate system and the body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the target image.
  • the method further includes: processing the target image and the position information of the pixels in the target image through the gesture recognition model to obtain shape parameters and displacement parameters, and the posture parameters, shape parameters and displacement parameters are used in common to obtain the pose of the target object.
  • the method further includes: performing normalization processing on the position information of the pixels in the target image to obtain the normalized position information; Processing the position information of the point to obtain the attitude parameters includes: processing the target image and the normalized position information through the attitude recognition model to obtain the attitude parameters.
  • the target object is a human body.
  • the third aspect of the embodiment of the present application provides a model training method, the method includes: acquiring the image to be processed; processing the target area of the image to be processed by the model to be trained and the position information of the target area in the image to be processed, to obtain Attitude parameters, the target area is the area where the target object is located; based on the attitude parameters, the predicted attitude of the target object is obtained; based on the predicted attitude of the target object and the real attitude of the target object, the training model is trained to obtain the attitude recognition model.
  • the gesture recognition model obtained by the above method can make the gesture recognition model perceive the position of the target object in the whole image, and learn the relationship between the position and the pose of the target object, so as to capture the pose of the target object more accurately.
  • the input of the gesture recognition model not only includes the cropped target area, but also includes the position information of the target area in the target image, so the gesture recognition model not only considers the image information of the target area itself.
  • the influence caused by the posture of the target object also takes into account the influence of the position information of the target area in the target image on the posture of the target object.
  • the factors considered are relatively comprehensive, so the posture of the target object obtained based on this method, It has higher accuracy, which is beneficial to improve the accuracy of the acquired motion information of the target image.
  • the model to be trained is trained based on the predicted pose of the target object and the real pose of the target object
  • the gesture recognition model obtained includes: a predicted projection result based on the predicted pose of the target object on the image to be processed and The real projection result of the real pose of the target object on the image to be processed is used to train the model to be trained to obtain a pose recognition model.
  • the location information includes the coordinates of the center point of the target area in the image coordinate system and the size of the target area, and the image coordinate system is constructed based on the image to be processed.
  • the location information includes coordinates of vertices of the target area in an image coordinate system, and the image coordinate system is constructed based on the image to be processed.
  • the predicted pose of the target object includes the predicted orientation of the target object in the camera coordinate system and the predicted body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the image to be processed.
  • the method includes: processing the target area of the image to be processed by the model to be trained and the position information of the target area in the image to be processed to obtain shape parameters and displacement parameters, attitude parameters, shape parameters and The displacement parameters are collectively used to obtain the pose of the target object.
  • the method further includes: performing normalization processing on the position information of the target area in the image to be processed to obtain the normalized position information;
  • processing the position information of the target area in the image to be processed to obtain the attitude parameters includes: processing the target area of the image to be processed by the model to be trained and the normalized position information to obtain the attitude parameters.
  • the target object is a human body.
  • the fourth aspect of the embodiments of the present application provides a model training method, the method comprising: acquiring an image to be processed;
  • the attitude parameters are obtained by processing the image to be processed and the position information of the pixels in the image to be processed by the model to be trained; based on the attitude parameters, the predicted attitude of the target object is obtained; based on the predicted attitude of the target object and the real attitude of the target object, the attitude parameter
  • the model is trained to obtain a gesture recognition model.
  • the gesture recognition model obtained by the above method can make the gesture recognition model perceive the position of the target object in the whole image, and learn the relationship between the position and the pose of the target object, so as to capture the pose of the target object more accurately.
  • the pose recognition model is in image processing, its input not only includes the target image, but also includes the position information of the pixels in the target image, so the pose recognition model not only considers the influence of the image information of the target image itself on the pose of the target object , it also takes into account the impact of the position information of the pixel points in the target image on the attitude of the target object, and the factors considered are relatively comprehensive, so the attitude of the target object obtained based on this method has high accuracy, and further has It is beneficial to improve the accuracy of the acquired motion information of the target image.
  • the model to be trained is trained to obtain a pose recognition model: based on the predicted projection result of the predicted pose of the target object on the image to be processed and the target The real projection result of the real pose of the object on the image to be processed is used to train the model to be trained to obtain a pose recognition model.
  • the location information includes the coordinates of the pixel points in the image coordinate system, and the image coordinate system is constructed based on the image to be processed.
  • the predicted pose of the target object includes the predicted orientation of the target object in the camera coordinate system and the predicted body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the image to be processed.
  • the method further includes: processing the target area of the image to be processed by the model to be trained and the position information of the pixels in the image to be processed to obtain attitude parameters, shape parameters and displacement parameters, attitude parameters, The shape parameter and the displacement parameter are jointly used to obtain the pose of the target object.
  • the method further includes: performing normalization processing on the position information of the pixels in the image to be processed to obtain the normalized position information; Processing the position information of the pixels to obtain the attitude parameters includes: processing the image to be processed and the normalized position information by the model to be trained to obtain the attitude parameters.
  • the target object is a human body.
  • the fifth aspect of the embodiment of the present application provides a gesture recognition device, the device includes: an acquisition module, used to acquire a target image; The position information in is processed to obtain the attitude parameters, the target area is the area where the target object is located, and the attitude parameters are used to obtain the attitude of the target object.
  • the target area where the target object is located in the target image and the position information of the target area in the target image can be input into the gesture recognition model, so that the target image of the target image can be detected by the gesture recognition model.
  • the position information of the area and the target area in the target image is processed to obtain the pose parameters, so the pose of the target object can be obtained based on the pose parameters.
  • the input of the gesture recognition model not only includes the cropped target area, but also includes the position information of the target area in the target image, so the gesture recognition model not only considers the image information of the target area itself when performing image processing
  • the impact on the posture of the target object also takes into account the influence of the position information of the target area in the target image on the posture of the target object.
  • the factors considered are relatively comprehensive, so the posture of the target object obtained based on this method , has higher accuracy, which is beneficial to improve the accuracy of the acquired motion information of the target image.
  • the location information includes the coordinates of the center point of the target area in the image coordinate system and the size of the target area, and the image coordinate system is constructed based on the target image.
  • the location information includes coordinates of vertices of the target area in an image coordinate system, and the image coordinate system is constructed based on the target image.
  • the pose recognition model is obtained by training based on a predicted projection result of the predicted pose of the target object on the image to be processed and a real projection result of the real pose of the target object on the image to be processed.
  • the pose of the target object includes the orientation of the target object in the camera coordinate system and the body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the target image.
  • the processing module is also used to process the target area of the target image and the position information of the target area in the target image through the pose recognition model to obtain shape parameters and displacement parameters, pose parameters, shape parameters Together with the displacement parameter, it is used to obtain the pose of the target object.
  • the device further includes: a normalization module, configured to perform normalization processing on the position information of the target area in the target image to obtain normalized position information;
  • the target area of the target image and the normalized position information are processed by the gesture recognition model to obtain the gesture parameters.
  • the target object is a human body.
  • the sixth aspect of the embodiment of the present application provides a gesture recognition device, which includes: an acquisition module, used to acquire a target image; a processing module, used to use a gesture recognition model to process the target image and the position information of the pixels in the target image Processing is performed to obtain attitude parameters, and the attitude parameters are used to obtain the attitude of the target object included in the target image.
  • the target image and the position information of the pixels in the target image can be input to the gesture recognition model, so as to perform the target image and the position information of the pixels in the target image through the gesture recognition model.
  • the posture parameters are obtained, so the posture of the target object presented by the target image can be obtained based on the posture parameters.
  • the input of the gesture recognition model not only includes the target image, but also includes the position information of the pixels in the target image. Therefore, when the gesture recognition model performs image processing, it not only considers the image information of the target image itself to the pose of the target object.
  • the impact caused by this method also takes into account the influence of the position information of the pixel points in the target image on the attitude of the target object.
  • the factors considered are relatively comprehensive, so the attitude of the target object obtained based on this method has high accuracy. degree, which in turn helps to improve the accuracy of the acquired motion information of the target image.
  • the position information includes the coordinates of the pixel points in the image coordinate system, and the image coordinate system is constructed based on the target image.
  • the pose recognition model is obtained by training based on a predicted projection result of the predicted pose of the target object on the image to be processed and a real projection result of the real pose of the target object on the image to be processed.
  • the pose of the target object includes the orientation of the target object in the camera coordinate system and the body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the target image.
  • the processing module is also used to process the target image and the position information of the pixels in the target image through the gesture recognition model to obtain the shape parameter and the displacement parameter, and the gesture parameter, the shape parameter and the displacement parameter are jointly Used to get the pose of the target object.
  • the device further includes: a normalization module, configured to perform normalization processing on the position information of pixels in the target image to obtain normalized position information; a processing module, configured to The target image and the normalized position information are processed by the gesture recognition model to obtain the gesture parameters.
  • a normalization module configured to perform normalization processing on the position information of pixels in the target image to obtain normalized position information
  • a processing module configured to The target image and the normalized position information are processed by the gesture recognition model to obtain the gesture parameters.
  • the target object is a human body.
  • the seventh aspect of the embodiment of the present application provides a model training device, the device includes: a first acquisition module, used to acquire the image to be processed; a processing module, used to use the model to be trained to target the target area of the image to be processed and the target area Process the position information in the image to be processed to obtain attitude parameters, and the target area is the area where the target object is located;
  • the second acquisition module is used to acquire the predicted pose of the target object based on the pose parameters; the training module is used to train the model to be trained based on the predicted pose of the target object and the real pose of the target object to obtain a pose recognition model.
  • the gesture recognition model obtained by the above-mentioned device can make the gesture recognition model perceive the position of the target object in the whole picture, and learn the relationship between the position and the posture of the target object, so as to capture the posture of the target object more accurately.
  • the input of the gesture recognition model not only includes the cropped target area, but also includes the position information of the target area in the target image, so the gesture recognition model not only considers the image information of the target area itself.
  • the influence caused by the posture of the target object also takes into account the influence of the position information of the target area in the target image on the posture of the target object.
  • the factors considered are relatively comprehensive, so the posture of the target object obtained based on this method, It has higher accuracy, which is beneficial to improve the accuracy of the acquired motion information of the target image.
  • the training module is configured to train the model to be trained based on the predicted projection result of the predicted pose of the target object on the image to be processed and the real projection result of the real pose of the target object on the image to be processed , to get the gesture recognition model.
  • the location information includes the coordinates of the center point of the target area in the image coordinate system and the size of the target area, and the image coordinate system is constructed based on the image to be processed.
  • the location information includes coordinates of vertices of the target area in an image coordinate system, and the image coordinate system is constructed based on the image to be processed.
  • the predicted pose of the target object includes the predicted orientation of the target object in the camera coordinate system and the predicted body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the image to be processed.
  • the processing module is also used to process the target area of the image to be processed and the position information of the target area in the image to be processed through the pose recognition model to obtain shape parameters and displacement parameters, pose parameters, shape The parameters and the displacement parameters are used together to obtain the pose of the target object.
  • the device further includes: a normalization module, configured to perform normalization processing on the position information of the target area in the image to be processed to obtain normalized position information; a processing module, It is used to process the target area of the image to be processed by the model to be trained and the normalized position information to obtain the pose parameters.
  • a normalization module configured to perform normalization processing on the position information of the target area in the image to be processed to obtain normalized position information
  • a processing module It is used to process the target area of the image to be processed by the model to be trained and the normalized position information to obtain the pose parameters.
  • the target object is a human body.
  • the eighth aspect of the embodiment of the present application provides a model training device, the device includes: a first acquisition module, used to acquire the image to be processed; a processing module, used to use the model to be trained to process the image to be processed and the pixels in the image to be processed The position information of the point is processed to obtain the attitude parameters; the second acquisition module is used to obtain the predicted attitude of the target object based on the attitude parameters; the training module is used to treat the training model based on the predicted attitude of the target object and the real attitude of the target object Perform training to obtain a gesture recognition model.
  • the gesture recognition model obtained by the above-mentioned device can make the gesture recognition model perceive the position of the target object in the whole picture, and learn the relationship between the position and the posture of the target object, so as to capture the posture of the target object more accurately.
  • the pose recognition model is in image processing, its input not only includes the target image, but also includes the position information of the pixels in the target image, so the pose recognition model not only considers the influence of the image information of the target image itself on the pose of the target object , it also takes into account the influence of the position information of the pixel points in the target image on the attitude of the target object, and the factors considered are relatively comprehensive. Therefore, the attitude of the target object obtained based on this method has high accuracy, and further has It is beneficial to improve the accuracy of the acquired motion information of the target image.
  • the training module is configured to train the model to be trained based on the predicted projection result of the predicted pose of the target object on the image to be processed and the real projection result of the real pose of the target object on the image to be processed , to get the gesture recognition model.
  • the location information includes the coordinates of the pixel points in the image coordinate system, and the image coordinate system is constructed based on the image to be processed.
  • the predicted pose of the target object includes the predicted orientation of the target object in the camera coordinate system and the predicted body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the image to be processed.
  • the processing module is also used to process the target area of the image to be processed and the position information of the pixels in the image to be processed through the pose recognition model to obtain shape parameters and displacement parameters, pose parameters, shape parameters Together with the displacement parameter, it is used to obtain the pose of the target object.
  • the device further includes: a normalization module, configured to perform normalization processing on the position information of pixels in the image to be processed, to obtain normalized position information; a processing module, configured to The attitude parameters are obtained by processing the image to be processed and the normalized position information by the model to be trained.
  • a normalization module configured to perform normalization processing on the position information of pixels in the image to be processed, to obtain normalized position information
  • a processing module configured to The attitude parameters are obtained by processing the image to be processed and the normalized position information by the model to be trained.
  • the target object is a human body.
  • the ninth aspect of the embodiment of the present application provides a gesture recognition device, the device includes a memory and a processor; the memory stores codes, the processor is configured to execute the codes, and when the codes are executed, the gesture recognition device performs the first aspect, any one possible implementation manner of the first aspect, the second aspect, or the method described in any one possible implementation manner of the second aspect.
  • the tenth aspect of the embodiments of the present application provides a model training device, the device includes a memory and a processor; the memory stores codes, the processor is configured to execute the codes, and when the codes are executed, the model training device executes the third aspect, any one possible implementation manner of the third aspect, the fourth aspect, or the method described in any one possible implementation manner of the fourth aspect.
  • An eleventh aspect of the embodiments of the present application provides a circuit system, where the circuit system includes a processing circuit configured to perform any one of the possible implementations in the first aspect, the first aspect, the second aspect, Any one of the possible implementations of the second aspect, the third aspect, any one of the possible implementations of the third aspect, the fourth aspect, or the method described in any one of the possible implementations of the fourth aspect.
  • the twelfth aspect of the embodiments of the present application provides a chip system, the chip system includes a processor, used to call the computer program or computer instruction stored in the memory, so that the processor executes the first aspect, the first aspect
  • the chip system includes a processor, used to call the computer program or computer instruction stored in the memory, so that the processor executes the first aspect, the first aspect
  • Any possible implementation of the second aspect, any possible implementation of the second aspect, the third aspect, any possible implementation of the third aspect, the fourth aspect or any of the fourth aspects The method described in one possible implementation.
  • the processor is coupled to the memory through an interface.
  • the chip system further includes a memory, where computer programs or computer instructions are stored.
  • the thirteenth aspect of the embodiments of the present application provides a computer storage medium, the computer storage medium stores a computer program, and when the program is executed by a computer, the computer implements any one of the possibilities in the first aspect and the first aspect.
  • the fourteenth aspect of the embodiments of the present application provides a computer program product, the computer program product stores instructions, and when the instructions are executed by a computer, the computer implements any one of the possible functions of the first aspect and the first aspect.
  • the computer program product stores instructions, and when the instructions are executed by a computer, the computer implements any one of the possible functions of the first aspect and the first aspect.
  • the target area where the target object is located in the target image and the position information of the target area in the target image can be input into the gesture recognition model, so that the target area of the target image can be identified by the gesture recognition model.
  • the position information of the target area in the target image is processed to obtain the pose parameters, so the pose of the target object can be obtained based on the pose parameters.
  • the input of the gesture recognition model not only includes the cropped target area, but also includes the position information of the target area in the target image, so the gesture recognition model not only considers the image information of the target area itself when performing image processing
  • the impact on the posture of the target object also takes into account the influence of the position information of the target area in the target image on the posture of the target object.
  • the factors considered are relatively comprehensive, so the posture of the target object obtained based on this method , has higher accuracy, which is beneficial to improve the accuracy of the acquired motion information of the target image.
  • Fig. 1 is a schematic diagram of related technology
  • Fig. 2 is another schematic diagram of related technology
  • Fig. 3 is a kind of structural schematic diagram of main frame of artificial intelligence
  • FIG. 4a is a schematic structural diagram of an image processing system provided by an embodiment of the present application.
  • Fig. 4b is another schematic structural diagram of the image processing system provided by the embodiment of the present application.
  • FIG. 4c is a schematic diagram of related equipment for image processing provided by the embodiment of the present application.
  • FIG. 5 is a schematic diagram of the architecture of the system 100 provided by the embodiment of the present application.
  • FIG. 6 is a schematic flowchart of a gesture recognition method provided in an embodiment of the present application.
  • FIG. 7 is a schematic diagram of an application example of gesture recognition provided by the embodiment of the present application.
  • FIG. 8 is another schematic flowchart of the gesture recognition method provided by the embodiment of the present application.
  • Fig. 9a is a schematic diagram of another application example of gesture recognition provided by the embodiment of the present application.
  • Fig. 9b is a schematic structural diagram of the gesture recognition model provided by the embodiment of the present application.
  • Fig. 10 is a schematic diagram of the model training method provided by the embodiment of the present application.
  • Fig. 11 is a schematic diagram of the projection result provided by the embodiment of the present application.
  • FIG. 12 is another schematic diagram of the model training method provided by the embodiment of the present application.
  • FIG. 13 is a schematic structural diagram of a gesture recognition device provided by an embodiment of the present application.
  • Fig. 14 is another schematic structural diagram of the gesture recognition device provided by the embodiment of the present application.
  • Fig. 15 is another structural schematic diagram of the model training device provided by the embodiment of the present application.
  • Fig. 16 is another structural schematic diagram of the model training device provided by the embodiment of the present application.
  • Fig. 17 is a schematic structural diagram of the execution device provided by the embodiment of the present application.
  • FIG. 18 is a schematic structural diagram of a training device provided in an embodiment of the present application.
  • FIG. 19 is a schematic structural diagram of a chip provided by an embodiment of the present application.
  • the embodiment of the present application provides a posture recognition method and related equipment, which can obtain the posture of the target object after processing the image containing the target object, which has high accuracy, and is conducive to improving the accuracy of the acquired target image. Accuracy of exercise information.
  • Human body motion capture technology is a commonly used technology in the film and television and game industries. This technology can process the input video stream to capture the posture of the human body in each frame of image, so as to obtain the motion information of the human body. Based on this technology, the posture data of human body movement can be extracted from massive videos, and has a wide range of application scenarios, for example, virtual characters in augmented reality (augmented reality, AR) scenes and virtual reality (virtual reality, VR) scenes drives, teleconferencing, and the metaverse, to name a few.
  • augmented reality augmented reality
  • VR virtual reality
  • human body motion capture can be realized based on AI technology.
  • the trained neural network can be used to process the area where the human body is located in the image, so as to obtain posture parameters, and the posture parameters can be used to determine the posture of the human body.
  • FIG. 1 is a schematic diagram of the related art
  • the human body if there are multiple images presenting the same environment, they are Image 1, Image 2, and Image 3 respectively.
  • the human body In the three images, the human body is located in three positions in the environment, and when they are located in different positions, the postures of the human body are extremely similar.
  • the neural network will determine that the posture parameters of the human body in the three images are consistent, that is, it is determined that the posture 1 of the human body in image 1, the posture 2 of the human body in image 2, and the posture 3 of the human body in image 3 are the same of.
  • Figure 2 is another schematic diagram of related technology
  • Figure 2 observes the three positions of the human body in the environment from the perspective of looking down.
  • the orientation of the human body is facing the camera sideways (tilted to the right).
  • the orientation of the human body is It is directly facing the camera.
  • the posture 3 of the human body the orientation of the human body is facing the camera sideways (leaning to the left). It can be seen that the difference between these three postures cannot be recognized by the neural network in the related technology, which leads to the inaccurate captured posture of the human body, which in turn affects the accuracy of the motion information of the human body.
  • an embodiment of the present application provides a gesture recognition method, which can be implemented in combination with artificial intelligence (AI) technology.
  • AI technology is a technical discipline that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence. AI technology obtains the best results by perceiving the environment, acquiring knowledge and using knowledge.
  • artificial intelligence technology is a branch of computer science that attempts to understand the nature of intelligence and produce a new kind of intelligent machine that can respond in a similar way to human intelligence.
  • Image processing using artificial intelligence is a common application of artificial intelligence.
  • Figure 3 is a schematic structural diagram of the main framework of artificial intelligence.
  • the following is from the “intelligent information chain” (horizontal axis) and “IT value chain” (vertical axis)
  • the "intelligent information chain” reflects a series of processes from data acquisition to processing. For example, it can be the general process of intelligent information perception, intelligent information representation and formation, intelligent reasoning, intelligent decision-making, intelligent execution and output. In this process, data has undergone a condensed process of "data-information-knowledge-wisdom”.
  • the "IT value chain” reflects the value brought by artificial intelligence to the information technology industry from the underlying infrastructure of artificial intelligence, information (provided and processed by technology) to the industrial ecological process of the system.
  • the infrastructure provides computing power support for the artificial intelligence system, realizes communication with the outside world, and realizes support through the basic platform.
  • the basic platform includes distributed computing framework and network and other related platform guarantees and supports, which can include cloud storage and Computing, interconnection network, etc.
  • sensors communicate with the outside to obtain data, and these data are provided to the smart chips in the distributed computing system provided by the basic platform for calculation.
  • Data from the upper layer of the infrastructure is used to represent data sources in the field of artificial intelligence.
  • the data involves graphics, images, voice, text, and IoT data of traditional equipment, including business data of existing systems and sensory data such as force, displacement, liquid level, temperature, and humidity.
  • Data processing usually includes data training, machine learning, deep learning, search, reasoning, decision-making, etc.
  • machine learning and deep learning can symbolize and formalize intelligent information modeling, extraction, preprocessing, training, etc. of data.
  • Reasoning refers to the process of simulating human intelligent reasoning in a computer or intelligent system, and using formalized information to carry out machine thinking and solve problems according to reasoning control strategies.
  • the typical functions are search and matching.
  • Decision-making refers to the process of decision-making after intelligent information is reasoned, and usually provides functions such as classification, sorting, and prediction.
  • some general capabilities can be formed based on the results of data processing, such as algorithms or a general system, such as translation, text analysis, computer vision processing, speech recognition, image processing identification, etc.
  • Intelligent products and industry applications refer to the products and applications of artificial intelligence systems in various fields. It is the packaging of the overall solution of artificial intelligence, which commercializes intelligent information decision-making and realizes landing applications. Its application fields mainly include: intelligent terminals, intelligent transportation, Smart healthcare, autonomous driving, smart cities, etc.
  • Fig. 4a is a schematic structural diagram of an image processing system provided by an embodiment of the present application, and the image processing system includes a user device and a data processing device.
  • the user equipment includes smart terminals such as a mobile phone, a personal computer, or an information processing center.
  • the user equipment is the initiator of the image processing, and as the initiator of the image processing request, usually the user initiates the request through the user equipment.
  • the above-mentioned data processing device may be a device or server having a data processing function such as a cloud server, a network server, an application server, and a management server.
  • the data processing device receives the image processing request from the intelligent terminal through the interactive interface, and then performs image processing such as machine learning, deep learning, search, reasoning, and decision-making through the memory for storing data and the processor link of data processing.
  • the storage in the data processing device can be a general term, including local storage and databases for storing historical data.
  • the database can be on the data processing device or on other network servers.
  • the user equipment can receive user instructions, for example, the user equipment can obtain an image input/selected by the user, and then initiate a request to the data processing equipment, so that the data processing equipment can obtain the user equipment.
  • An image processing application is performed on the image (for example, recognizing a pose of the target object, etc.), so as to obtain a corresponding processing result for the image.
  • the user device may acquire an image input by the user, and then initiate a gesture recognition request of the target object to the data processing device, so that the data processing device classifies the image, thereby obtaining the pose parameters of the target object in the image, thereby determining The pose of the target object in the image.
  • the data processing device can execute the image processing method of the embodiment of the present application.
  • Fig. 4b is another schematic structural diagram of the image processing system provided by the embodiment of the present application.
  • the user equipment is directly used as a data processing equipment, and the user equipment can directly obtain the input from the user and directly perform the processing by the hardware of the user equipment itself.
  • the specific process is similar to that in FIG. 4a , and reference may be made to the above description, and details are not repeated here.
  • the user equipment may receive an instruction from the user, for example, the user equipment may acquire an image selected by the user in the user equipment, and then the user equipment itself executes an image processing application (for example, the recognition gesture of the target object, etc.), so as to obtain the corresponding processing results for the image.
  • an image processing application For example, the recognition gesture of the target object, etc.
  • the user equipment itself can execute the gesture recognition method of the embodiment of the present application.
  • Fig. 4c is a schematic diagram of related equipment for image processing provided by the embodiment of the present application.
  • the above-mentioned user equipment in FIG. 4a and FIG. 4b may specifically be the local device 301 or local device 302 in FIG. 4c, and the data processing device in FIG. 4a may specifically be the execution device 210 in FIG.
  • the data storage system 250 may be integrated on the execution device 210, or set on the cloud or other network servers.
  • the processors in Figure 4a and Figure 4b can perform data training/machine learning/deep learning through a neural network model or other models (for example, a model based on a support vector machine), and use the data to finally train or learn the model for image execution Image processing application, so as to obtain the corresponding processing results.
  • a neural network model or other models for example, a model based on a support vector machine
  • FIG. 5 is a schematic diagram of the architecture of the system 100 provided by the embodiment of the present application.
  • the execution device 110 is configured with an input/output (I/O) interface 112 for data interaction with external devices, and the user Data can be input to the I/O interface 112 through the client device 140, and the input data in this embodiment of the application may include: various tasks to be scheduled, callable resources, and other parameters.
  • I/O input/output
  • the execution device 110 When the execution device 110 preprocesses the input data, or when the calculation module 111 of the execution device 110 executes calculations and other related processing (such as implementing the function of the neural network in this application), the execution device 110 can call the data storage system 150
  • the data, codes, etc. in the system can be used for corresponding processing, and the data, instructions, etc. obtained by corresponding processing can also be stored in the data storage system 150 .
  • the I/O interface 112 returns the processing result to the client device 140, thereby providing it to the user.
  • the training device 120 can generate corresponding target models/rules based on different training data for different goals or different tasks, and the corresponding target models/rules can be used to achieve the above-mentioned goals or complete the above-mentioned tasks , giving the user the desired result.
  • the training data can be stored in the database 130, and come from the training samples collected by the data collection device 160.
  • the user can manually specify the input data, and the manual specification can be operated through the interface provided by the I/O interface 112 .
  • the client device 140 can automatically send the input data to the I/O interface 112 . If the client device 140 is required to automatically send the input data to obtain the user's authorization, the user can set the corresponding authority in the client device 140 .
  • the user can view the results output by the execution device 110 on the client device 140, and the specific presentation form may be specific ways such as display, sound, and action.
  • the client device 140 can also be used as a data collection terminal, collecting the input data input to the I/O interface 112 as shown in the figure and the output results of the output I/O interface 112 as new sample data, and storing them in the database 130 .
  • the I/O interface 112 directly uses the input data input to the I/O interface 112 as shown in the figure and the output result of the output I/O interface 112 as a new sample The data is stored in database 130 .
  • FIG. 5 is only a schematic diagram of a system architecture provided by the embodiment of the present application, and the positional relationship between devices, devices, modules, etc. shown in the figure does not constitute any limitation.
  • the data The storage system 150 is an external memory relative to the execution device 110 , and in other cases, the data storage system 150 may also be placed in the execution device 110 .
  • the neural network can be obtained by training according to the training device 120 .
  • An embodiment of the present application also provides a chip, the chip includes a neural network processor (NPU).
  • the chip can be set in the execution device 110 shown in FIG. 5 to complete the computing work of the computing module 111 .
  • the chip can also be set in the training device 120 shown in FIG. 5 to complete the training work of the training device 120 and output the target model/rule.
  • NPU neural network processor
  • the neural network processor NPU is mounted on the main central processing unit (central processing unit, CPU) (host CPU) as a coprocessor, and the main CPU assigns tasks.
  • the core part of the NPU is the operation circuit, and the controller controls the operation circuit to extract the data in the memory (weight memory or input memory) and perform operations.
  • the operation circuit includes multiple processing units (process engine, PE).
  • the arithmetic circuit is a two-dimensional systolic array.
  • the arithmetic circuit may also be a one-dimensional systolic array or other electronic circuitry capable of performing mathematical operations such as multiplication and addition.
  • the arithmetic circuit is a general purpose matrix processor.
  • the operation circuit fetches the data corresponding to the matrix B from the weight memory, and caches it on each PE in the operation circuit.
  • the operation circuit takes the data of matrix A from the input memory and performs matrix operation with matrix B, and the obtained partial or final results of the matrix are stored in the accumulator.
  • the vector calculation unit can further process the output of the operation circuit, such as vector multiplication, vector addition, exponential operation, logarithmic operation, size comparison and so on.
  • the vector computing unit can be used for network calculations of non-convolution/non-FC layers in neural networks, such as pooling, batch normalization, local response normalization, etc.
  • the vector computation unit can store the processed output vectors to a unified register.
  • a vector computation unit may apply a non-linear function to the output of the arithmetic circuit, such as a vector of accumulated values, to generate activation values.
  • the vector computation unit generates normalized values, merged values, or both.
  • the vector of processed outputs can be used as an activation input to an operational circuit, for example for use in a subsequent layer in a neural network.
  • Unified memory is used to store input data and output data.
  • the weight data directly transfers the input data in the external memory to the input memory and/or the unified memory through the storage unit access controller (direct memory access controller, DMAC), stores the weight data in the external memory into the weight memory, and stores the weight data in the unified memory Store the data in the external memory.
  • DMAC direct memory access controller
  • bus interface unit (bus interface unit, BIU) is used to realize the interaction between the main CPU, DMAC and instruction fetch memory through the bus.
  • the instruction fetch buffer connected to the controller is used to store the instructions used by the controller
  • the controller is used for invoking instructions cached in the memory to control the working process of the computing accelerator.
  • the unified memory, the input memory, the weight memory and the instruction fetch memory are all on-chip (On-Chip) memory
  • the external memory is the memory outside the NPU
  • the external memory can be a double data rate synchronous dynamic random access memory (double data rate synchronous dynamic random access memory, DDR SDRAM), high bandwidth memory (high bandwidth memory, HBM) or other readable and writable memory.
  • the neural network can be composed of neural units, and the neural unit can refer to an operation unit that takes xs and intercept 1 as input, and the output of the operation unit can be:
  • Ws is the weight of xs
  • b is the bias of the neuron unit.
  • f is the activation function of the neural unit, which is used to introduce nonlinear characteristics into the neural network to convert the input signal in the neural unit into an output signal. The output signal of this activation function can be used as the input of the next convolutional layer.
  • the activation function may be a sigmoid function.
  • a neural network is a network formed by connecting many of the above-mentioned single neural units, that is, the output of one neural unit can be the input of another neural unit.
  • the input of each neural unit can be connected with the local receptive field of the previous layer to extract the features of the local receptive field.
  • the local receptive field can be an area composed of several neural units.
  • W is a weight vector, and each value in the vector represents the weight value of a neuron in this layer of neural network.
  • the vector W determines the space transformation from the input space to the output space described above, that is, the weight W of each layer controls how to transform the space.
  • the purpose of training the neural network is to finally obtain the weight matrix of all layers of the trained neural network (the weight matrix formed by the vector W of many layers). Therefore, the training process of the neural network is essentially to learn the way to control the space transformation, and more specifically, to learn the weight matrix.
  • the neural network can use the error back propagation (back propagation, BP) algorithm to correct the size of the parameters in the initial neural network model during the training process, so that the reconstruction error loss of the neural network model becomes smaller and smaller. Specifically, passing the input signal forward until the output will generate an error loss, and updating the parameters in the initial neural network model by backpropagating the error loss information, so that the error loss converges.
  • the backpropagation algorithm is a backpropagation movement dominated by error loss, aiming to obtain the optimal parameters of the neural network model, such as the weight matrix.
  • the model training method provided in the embodiment of the present application involves image processing, and can be specifically applied to data processing methods such as data training, machine learning, and deep learning to symbolize and form the training data (such as the image to be processed in this application) intelligent information modeling, extraction, preprocessing, training, etc., and finally a trained neural network (such as the gesture recognition model in this application); and, the gesture recognition method provided in the embodiment of this application can use the above-mentioned trained A neural network, inputting input data (such as the target image in the present application, the target area of the target image) into the trained neural network to obtain output data (such as the pose parameters in the present application, etc.).
  • the model training method and gesture recognition method provided in the embodiment of the present application are inventions based on the same idea, and can also be understood as two parts in a system, or two stages of an overall process: such as model training phase and model application phase.
  • Fig. 6 is a schematic flow chart of the gesture recognition method provided by the embodiment of the present application. As shown in Fig. 6, the method includes:
  • the target image when it is necessary to perform gesture recognition on the target image, the target image may be acquired first, and the target image may be a certain frame image in the video stream, or may be a single image.
  • the target image usually presents the target object and the environment in which the target object is located, so the goal of gesture recognition is to obtain the pose of the target object in the target image.
  • Figure 7 Figure 7 is a schematic diagram of an application example of gesture recognition provided by the embodiment of the present application
  • the content presented by the target image is the human body and the environment in which the human body is located, so the gesture recognition target of the target image is Recognize the pose of the human body in the target image.
  • the target image can be preprocessed. Specifically, the target image can be detected to determine the area where the target object is located in the target image, and this area can be called a target area (also called a detection frame). Still as in the above example, after the area where the human body is located in the target image is detected, the area where the human body is located can be determined as the target area (that is, the area enclosed by the red frame in FIG. 7 ).
  • the position information of the target area in the target image can also be obtained, and the position information can have many situations: (1)
  • the position information of the target area in the target image includes the coordinates of the center point of the target area in the image coordinate system As well as the size of the target area, the image coordinate system is constructed based on the target image. Specifically, a certain vertex of the target image (for example, the vertex in the upper left corner of the target image) can be used as the origin of the image coordinate system, then the coordinates of the center point of the target area in the image coordinate system can be determined, as well as the length of the target area and width (which is equivalent to obtaining the size of the target area), these information can be used to indicate the position of the target area in the target image.
  • a certain vertex of the target image for example, the vertex in the upper left corner of the target image
  • the coordinates of the center point of the target area in the image coordinate system can be determined, as well as the length of the target area and width (which is equivalent to obtaining the size of the
  • the position information of the target area in the target image includes the coordinates of the vertices of the target area in the image coordinate system, and the image coordinate system is constructed based on the target image. Specifically, a certain vertex of the target image (for example, the vertex in the upper left corner of the target image) can be used as the origin of the image coordinate system, then the coordinates of all vertices of the target area in the image coordinate system can be determined, and this information can be used to indicate The position of the target area in the target image and so on.
  • a certain vertex of the target image for example, the vertex in the upper left corner of the target image
  • this information can be used to indicate The position of the target area in the target image and so on.
  • the position information may also be normalized to obtain the normalized position information of the target area in the target image.
  • I the position information of the target area in the target image
  • I' the position information of the target area in the target image after normalization processing
  • I' the position information of the target area in the target image after normalization processing
  • I' the position information of the target area in the target image after normalization processing
  • I' the position information of the target area in the target image after normalization processing
  • I' (I-mean(I))/F
  • F sqrt(w*w+h*h)
  • mean (I) is the mean value of the target area
  • w the width of the target area
  • h the length of the target area.
  • the target area and the normalized position information of the target area in the target image can be input to the pose recognition model (for the trained neural network Network), to process the target area of the target image and the position information of the normalized target area in the target image through the gesture recognition model (for example, a series of operations such as feature extraction) to obtain the pose parameters. Then, the pose of the target object in the target image can be obtained based on the pose parameters.
  • the pose recognition model for the trained neural network Network
  • the gesture recognition model for example, a series of operations such as feature extraction
  • the pose recognition model can output not only pose parameters, but also shape parameters and displacement parameters.
  • the posture parameters are usually composed of two parts of parameters, one part of parameters is used to indicate the rotation angle of the target object relative to the camera, and the other part of parameters is used to indicate the angles between the joints of the target object itself.
  • the shape parameter is used to indicate the three-dimensional shape of the target object.
  • the displacement parameter is used to indicate the range occupied by the target object in the target area, and the offset of the target object in the target area (for example, taking the center point of the target area as a reference point, the degree to which the target object shifts to the left or to degree of right offset). Based on the calculation of attitude parameters, shape parameters and displacement parameters, the attitude of the target object can be obtained.
  • the attitude of the target object can be represented by multiple three-dimensional key points (3D key points) of the target object.
  • 3D key points can describe the position of the target object.
  • the orientation in the camera coordinate system that is, the orientation of the target object relative to the camera
  • the body behavior of the target object in the camera coordinate system that is, the movement of the target object in three-dimensional space
  • the origin of the camera coordinate system is the shooting target image camera.
  • the pose recognition model outputs pose parameters ⁇ , shape parameters ⁇ , and displacement parameters S, T
  • these parameters can be calculated to obtain multiple 3D key points of the target object.
  • These 3D key points The points are combined to show the orientation of the target object in the camera coordinate system and the body behavior of the target object in the camera coordinate system.
  • the gesture recognition of the target image is completed. If the target image is a certain frame image in the video stream, for the rest of the frame images in the video stream, the same operation as that performed on the target image can be performed to obtain the continuous multiple postures of the target object and form the motion information of the target object , so as to meet the various application needs of users.
  • the target area where the target object is located in the target image and the position information of the target area in the target image can be input into the gesture recognition model, so that the target area of the target image can be identified by the gesture recognition model.
  • the position information of the target area in the target image is processed to obtain the pose parameters, so the pose of the target object can be obtained based on the pose parameters.
  • the input of the gesture recognition model not only includes the cropped target area, but also includes the position information of the target area in the target image, so the gesture recognition model not only considers the image information of the target area itself when performing image processing
  • the impact on the posture of the target object also takes into account the influence of the position information of the target area in the target image on the posture of the target object.
  • the factors considered are relatively comprehensive, so the posture of the target object obtained based on this method , has higher accuracy, which is beneficial to improve the accuracy of the acquired motion information of the target image.
  • Fig. 8 is another schematic flowchart of the posture recognition method provided by the embodiment of the present application. As shown in Fig. 8, the method includes:
  • the target image when it is necessary to perform gesture recognition on the target image, the target image may be acquired first, and the target image may be a certain frame image in the video stream, or may be a single image.
  • the target image usually presents the target object and the environment in which the target object is located, so the goal of gesture recognition is to obtain the pose of the target object in the target image.
  • Figure 9a Figure 9a is a schematic diagram of another application example of gesture recognition provided by the embodiment of the present application
  • the content presented by the target image is the human body and the environment in which the human body is located, so the gesture recognition target of the target image In order to recognize the posture presented by the human body in the target image.
  • the position information of the pixel in the target image can also be obtained, the position information includes the coordinates of the target image and the pixel in the image coordinate system, and the image coordinate system is constructed based on the target image. Specifically, a certain vertex of the target image (for example, the vertex in the upper left corner of the target image) can be used as the origin of the image coordinate system, then the coordinates of all pixels in the target image in the image coordinate system can be determined, and these information can be used as pose The input of the recognition model, so that the input of the pose recognition model not only includes each pixel of the target image, but also includes the coordinates of each pixel in the target image in the image coordinate system.
  • the position information may also be normalized to obtain the position information of the pixels in the target image after normalization processing.
  • the positional information of the pixel in the target image is I
  • the positional information of the pixel in the target image after the normalization process is I'
  • I' (I-mean(I))/F
  • F sqrt(w ⁇ w+h ⁇ h) wherein, mean(I) is the mean value of the target image, w is the width of the target image, and h is the length of the target image.
  • the target image and the position information of the pixels in the normalized target image can be input to the gesture recognition model (a trained neural network) , to process the target area of the target image and the position information of the pixels in the normalized target image through the gesture recognition model (for example, a series of operations such as feature extraction) to obtain the gesture parameters. Then, the pose of the target object in the target image can be obtained based on the pose parameters.
  • the gesture recognition model a trained neural network
  • the gesture recognition model can include two parts, one part is an encoder (encoder), and the other part is a plurality of convolutional layers (convolution ).
  • the encoder receives the target image, it can perform feature extraction processing on the target image to obtain a feature image (featrue map), and send the feature image to the convolutional layer.
  • Multiple convolutional layers can perform at least one convolution process on the feature image and the position information (location map) of the target image after normalization to obtain the attitude parameters, so the position of the target object in the target image can be determined based on the attitude parameters. attitude.
  • the method further includes: processing the target image and the position information of the pixels in the target image through the gesture recognition model to obtain shape parameters and displacement parameters, and the posture parameters, shape parameters and displacement parameters are used in common to obtain the pose of the target object.
  • the pose of the target object includes the orientation of the target object in the camera coordinate system and the body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the target image.
  • step 802 For descriptions of step 802, reference may be made to relevant descriptions of step 602 in the embodiment shown in FIG. 6 , and details are not repeated here.
  • gesture recognition model can also have other various structures, as long as the gesture recognition function can be realized. Can.
  • the target image and the position information of the pixel points in the target image can be input to the gesture recognition model, so as to process the target image and the position information of the pixel points in the target image through the gesture recognition model , to obtain the pose parameter, so the pose of the target object presented by the target image can be obtained based on the pose parameter.
  • the input of the gesture recognition model not only includes the target image, but also includes the position information of the pixels in the target image. Therefore, when the gesture recognition model performs image processing, it not only considers the image information of the target image itself to the pose of the target object.
  • the impact caused by this method also takes into account the influence of the position information of the pixel points in the target image on the attitude of the target object.
  • the factors considered are relatively comprehensive, so the attitude of the target object obtained based on this method has high accuracy. degree, which in turn helps to improve the accuracy of the acquired motion information of the target image.
  • the gesture recognition model provided by the embodiment of the present application can also be compared with the gesture recognition model of the related art, and the comparison results are shown in Table 1 and Table 2:
  • the posture recognition model provided by the embodiment of the present application has significantly reduced various error indicators, that is, the posture recognition model provided by the embodiment of the present application has a significant reduction in each error index. All the error indicators have excellent performance.
  • the posture recognition model provided by the embodiment of the present application has significantly reduced various error indicators in the data set 1 and data set 2, that is, the error index provided by the embodiment of the present application
  • the gesture recognition model also has superior performance in various error indicators.
  • Fig. 10 is a schematic diagram of the model training method provided by the embodiment of the present application. As shown in Fig. 10, the method includes:
  • a batch of training samples can be obtained, that is, images to be processed for training. It is worth noting that, for the image to be processed, the real pose of the target object in the image to be processed is known.
  • the image to be processed can be detected and processed to determine the target area in the image to be processed.
  • the location information of the target area in the image to be processed can also be obtained and normalized.
  • the target object is a human body.
  • the position information of the target area in the image to be processed includes the coordinates of the center point of the target area in the image coordinate system and the size of the target area, and the image coordinate system is constructed based on the image to be processed.
  • the position information of the target area in the image to be processed includes coordinates of vertices of the target area in an image coordinate system, and the image coordinate system is constructed based on the image to be processed.
  • step 100 For descriptions of step 1001, reference may be made to relevant descriptions of step 601 in the embodiment shown in FIG. 6 , and details are not repeated here.
  • the image to be processed and the position information of the normalized target area in the image to be processed can be input to the model to be trained to
  • the pose parameters are obtained by processing the target area of the image to be processed by the model to be trained and the normalized position information of the target area in the image to be processed.
  • the method further includes: processing the target area of the image to be processed and the normalized position information of the target area in the image to be processed by the gesture recognition model to obtain shape parameters and displacement parameters, Pose parameters, shape parameters and displacement parameters are jointly used to obtain the pose of the target object.
  • step 1002 For the description of step 1002, reference may be made to the related description of step 602 in the embodiment shown in FIG. 6 , which will not be repeated here.
  • the calculation can be performed based on the attitude parameters, shape parameters and displacement parameters to obtain the predicted attitude of the target object. express.
  • the predicted pose of the target object includes the predicted orientation of the target object in the camera coordinate system and the predicted body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the image to be processed.
  • step 1003 For the description of step 1003, reference may be made to the related description of step 602 in the embodiment shown in FIG. 6 , and details are not repeated here.
  • the model to be trained can be trained based on the predicted pose of the target object and the real pose of the target object to obtain a pose recognition model.
  • the model to be trained is trained based on the predicted pose of the target object and the real pose of the target object
  • the gesture recognition model obtained includes: a predicted projection result based on the predicted pose of the target object on the image to be processed and The real projection result of the real pose of the target object on the image to be processed is used to train the model to be trained to obtain a pose recognition model.
  • Figure 11 is a schematic diagram of the projection result provided by the embodiment of the present application
  • the predicted pose of the target object can be represented by multiple predicted 3D key points of the target object
  • the predicted pose of the target object The posture can be represented by multiple real 3D key points of the target object (these real 3D key points can be marked in advance and are known), so multiple predicted 3D key points of the target object can be projected onto the image to be processed, Obtain multiple predicted two-dimensional key points of the target object (that is, the predicted projection result of the predicted pose of the target object on the image to be processed), and similarly, multiple real three-dimensional key points of the target object can also be projected onto the image to be processed , to obtain multiple real two-dimensional key points of the target object (that is, the real projection result of the real pose of the target object on the image to be processed).
  • the target loss can be calculated based on multiple predicted 2D key points and multiple real 2D key points of the target object, and the target loss is used to indicate multiple predicted 2D key points and multiple real 2D key points of the target object difference between.
  • the model parameters of the model to be trained can be updated based on the target loss, and the next batch of training samples can be used to train the model to be trained after the updated parameters (that is, re-execute steps 1002 to 1004), until the model training conditions are met (such as , the target loss reaches convergence, etc.), the gesture recognition model in the embodiment shown in FIG. 6 can be obtained.
  • the gesture recognition model trained in the embodiment of the present application can make the gesture recognition model perceive the position of the target object in the whole picture, and learn the relationship between the position and the pose of the target object, so as to more accurately capture the pose of the target object.
  • the input of the gesture recognition model not only includes the cropped target area, but also includes the position information of the target area in the target image, so the gesture recognition model not only considers the image information of the target area itself.
  • the influence caused by the posture of the target object also takes into account the influence of the position information of the target area in the target image on the posture of the target object.
  • the factors considered are relatively comprehensive, so the posture of the target object obtained based on this method, It has higher accuracy, which is beneficial to improve the accuracy of the acquired motion information of the target image.
  • Fig. 12 is another schematic diagram of the model training method provided by the embodiment of the present application. As shown in Fig. 12, the method includes:
  • a batch of training samples can be obtained, that is, images to be processed for training. It is worth noting that, for the image to be processed, the real pose of the target object in the image to be processed is known.
  • the position information of the pixels in the target area can be obtained and normalized.
  • the target object is a human body.
  • the position information of the pixels in the image to be processed includes coordinates of the pixels in an image coordinate system, and the image coordinate system is constructed based on the image to be processed.
  • step 1201 For the description of step 1201, reference may be made to the related description of step 801 in the embodiment shown in FIG. 8 , and details are not repeated here.
  • the position information of the pixels in the image to be processed and the normalized image to be processed can be input to the model to be trained, so as to pass the
  • the training model processes the target area of the image to be processed and the normalized position information of the pixels in the image to be processed to obtain the pose parameters.
  • the method further includes: processing the target area of the image to be processed and the normalized position information of pixels in the image to be processed by the gesture recognition model to obtain shape parameters and displacement parameters, and pose parameters, shape parameters and displacement parameters are jointly used to obtain the pose of the target object.
  • step 1202 For the description of step 1202, reference may be made to the relevant description of step 802 in the embodiment shown in FIG. 8 , and details are not repeated here.
  • the calculation can be performed based on the attitude parameters, shape parameters and displacement parameters to obtain the predicted attitude of the target object. express.
  • the predicted pose of the target object includes the predicted orientation of the target object in the camera coordinate system and the predicted body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the image to be processed.
  • step 1203 For descriptions of step 1203, reference may be made to relevant descriptions of step 1003 in the embodiment shown in FIG. 10 , and details are not repeated here.
  • the model to be trained can be trained based on the predicted pose of the target object and the real pose of the target object, and the pose recognition in the embodiment shown in Figure 8 is obtained Model.
  • the model to be trained is trained to obtain a pose recognition model: based on the predicted projection result of the predicted pose of the target object on the image to be processed and the target The real projection result of the real pose of the object on the image to be processed is used to train the model to be trained to obtain a pose recognition model.
  • step 1204 For the description of step 1204, reference may be made to the related description of step 1004 in the embodiment shown in FIG. 10 , which will not be repeated here.
  • the gesture recognition model trained in the embodiment of the present application can make the gesture recognition model perceive the position of the target object in the whole picture, and learn the relationship between the position and the pose of the target object, so as to more accurately capture the pose of the target object.
  • the pose recognition model is in image processing, its input not only includes the target image, but also includes the position information of the pixels in the target image, so the pose recognition model not only considers the influence of the image information of the target image itself on the pose of the target object , it also takes into account the impact of the position information of the pixel points in the target image on the attitude of the target object, and the factors considered are relatively comprehensive, so the attitude of the target object obtained based on this method has high accuracy, and further has It is beneficial to improve the accuracy of the acquired motion information of the target image.
  • Fig. 13 is a schematic structural diagram of the gesture recognition device provided by the embodiment of the present application. As shown in Fig. 13, the device includes:
  • the processing module 1302 is used to process the target area of the target image and the position information of the target area in the target image through the gesture recognition model to obtain gesture parameters, the target zone is the area where the target object is located, and the gesture parameters are used to obtain the target object attitude.
  • the target area where the target object is located in the target image and the position information of the target area in the target image can be input into the gesture recognition model, so that the target area of the target image can be identified by the gesture recognition model.
  • the position information of the target area in the target image is processed to obtain the pose parameters, so the pose of the target object can be obtained based on the pose parameters.
  • the input of the gesture recognition model not only includes the cropped target area, but also includes the position information of the target area in the target image, so the gesture recognition model not only considers the image information of the target area itself when performing image processing
  • the impact on the posture of the target object also takes into account the influence of the position information of the target area in the target image on the posture of the target object.
  • the factors considered are relatively comprehensive, so the posture of the target object obtained based on this method , has higher accuracy, which is beneficial to improve the accuracy of the acquired motion information of the target image.
  • the location information includes the coordinates of the center point of the target area in the image coordinate system and the size of the target area, and the image coordinate system is constructed based on the target image.
  • the location information includes coordinates of vertices of the target area in an image coordinate system, and the image coordinate system is constructed based on the target image.
  • the pose recognition model is obtained by training based on a predicted projection result of the predicted pose of the target object on the image to be processed and a real projection result of the real pose of the target object on the image to be processed.
  • the pose of the target object includes the orientation of the target object in the camera coordinate system and the body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the target image.
  • the processing module 1302 is also configured to process the target area of the target image and the position information of the target area in the target image through the gesture recognition model to obtain shape parameters and displacement parameters, gesture parameters, shape The parameters and the displacement parameters are used together to obtain the pose of the target object.
  • the device further includes: a normalization module, configured to perform normalization processing on the position information of the target area in the target image to obtain normalized position information; the processing module 1302, It is used to process the target area of the target image and the normalized position information through the gesture recognition model to obtain the gesture parameters.
  • a normalization module configured to perform normalization processing on the position information of the target area in the target image to obtain normalized position information
  • the processing module 1302 It is used to process the target area of the target image and the normalized position information through the gesture recognition model to obtain the gesture parameters.
  • the target object is a human body.
  • Fig. 14 is another schematic structural diagram of the gesture recognition device provided by the embodiment of the present application. As shown in Fig. 14, the device includes:
  • the processing module 1402 is configured to process the target image and the position information of the pixels in the target image through the gesture recognition model to obtain a gesture parameter, and the gesture parameter is used to acquire the gesture of the target object included in the target image.
  • the target image and the position information of the pixel points in the target image can be input to the gesture recognition model, so as to process the target image and the position information of the pixel points in the target image through the gesture recognition model , to obtain the pose parameter, so the pose of the target object presented by the target image can be obtained based on the pose parameter.
  • the input of the gesture recognition model not only includes the target image, but also includes the position information of the pixels in the target image. Therefore, when the gesture recognition model performs image processing, it not only considers the image information of the target image itself to the pose of the target object.
  • the impact caused by this method also takes into account the influence of the position information of the pixel points in the target image on the attitude of the target object.
  • the factors considered are relatively comprehensive, so the attitude of the target object obtained based on this method has high accuracy. degree, which in turn helps to improve the accuracy of the acquired motion information of the target image.
  • the position information includes the coordinates of the pixel points in the image coordinate system, and the image coordinate system is constructed based on the target image.
  • the pose recognition model is obtained by training based on a predicted projection result of the predicted pose of the target object on the image to be processed and a real projection result of the real pose of the target object on the image to be processed.
  • the pose of the target object includes the orientation of the target object in the camera coordinate system and the body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the target image.
  • the processing module 1402 is also configured to process the target image and the position information of the pixels in the target image through the pose recognition model to obtain shape parameters and displacement parameters, and pose parameters, shape parameters and displacement parameters Commonly used to obtain the pose of the target object.
  • the device further includes: a normalization module, configured to perform normalization processing on the position information of pixels in the target image to obtain normalized position information; the processing module 1402 uses The target image and the normalized position information are processed by the gesture recognition model to obtain the gesture parameters.
  • a normalization module configured to perform normalization processing on the position information of pixels in the target image to obtain normalized position information
  • the processing module 1402 uses The target image and the normalized position information are processed by the gesture recognition model to obtain the gesture parameters.
  • the target object is a human body.
  • Fig. 15 is another structural schematic diagram of the model training device provided by the embodiment of the present application. As shown in Fig. 15, the device includes:
  • the processing module 1502 is used to process the target area of the image to be processed by the model to be trained and the position information of the target area in the image to be processed to obtain posture parameters, and the target area is the area where the target object is located;
  • the second acquiring module 1503 is configured to acquire the predicted attitude of the target object based on the attitude parameter
  • the training module 1504 is configured to train the model to be trained based on the predicted pose of the target object and the real pose of the target object to obtain a pose recognition model.
  • the gesture recognition model obtained in the embodiment of the present application can make the gesture recognition model perceive the position of the target object in the whole picture, and learn the relationship between the position and the pose of the target object, so as to capture the pose of the target object more accurately.
  • the input of the gesture recognition model not only includes the cropped target area, but also includes the position information of the target area in the target image, so the gesture recognition model not only considers the image information of the target area itself.
  • the influence caused by the posture of the target object also takes into account the influence of the position information of the target area in the target image on the posture of the target object.
  • the factors considered are relatively comprehensive, so the posture of the target object obtained based on this method, It has higher accuracy, which is beneficial to improve the accuracy of the acquired motion information of the target image.
  • the training module 1504 is configured to perform the training on the model to be trained based on the predicted projection result of the predicted pose of the target object on the image to be processed and the real projection result of the real pose of the target object on the image to be processed. Training to get the gesture recognition model.
  • the location information includes the coordinates of the center point of the target area in the image coordinate system and the size of the target area, and the image coordinate system is constructed based on the image to be processed.
  • the location information includes coordinates of vertices of the target area in an image coordinate system, and the image coordinate system is constructed based on the image to be processed.
  • the predicted pose of the target object includes the predicted orientation of the target object in the camera coordinate system and the predicted body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the image to be processed.
  • the processing module 1502 is also configured to process the target area of the image to be processed and the position information of the target area in the image to be processed through the gesture recognition model to obtain shape parameters and displacement parameters, pose parameters, The shape parameter and the displacement parameter are jointly used to obtain the pose of the target object.
  • the device further includes: a normalization module, configured to perform normalization processing on the position information of the target area in the image to be processed to obtain the normalized position information; the processing module 1502 , which is used to process the target area of the image to be processed and the normalized position information through the model to be trained to obtain the pose parameters.
  • a normalization module configured to perform normalization processing on the position information of the target area in the image to be processed to obtain the normalized position information
  • the processing module 1502 which is used to process the target area of the image to be processed and the normalized position information through the model to be trained to obtain the pose parameters.
  • the target object is a human body.
  • Fig. 16 is another structural schematic diagram of the model training device provided by the embodiment of the present application. As shown in Fig. 16, the device includes:
  • the processing module 1602 is used to process the image to be processed and the position information of the pixels in the image to be processed through the model to be trained to obtain the attitude parameter;
  • the second acquiring module 1603 is configured to acquire the predicted attitude of the target object based on the attitude parameter
  • the training module 1604 is configured to train the model to be trained based on the predicted pose of the target object and the real pose of the target object to obtain a pose recognition model.
  • the gesture recognition model obtained in the embodiment of the present application can make the gesture recognition model perceive the position of the target object in the whole picture, and learn the relationship between the position and the pose of the target object, so as to capture the pose of the target object more accurately.
  • the pose recognition model is in image processing, its input not only includes the target image, but also includes the position information of the pixels in the target image, so the pose recognition model not only considers the influence of the image information of the target image itself on the pose of the target object , it also takes into account the impact of the position information of the pixel points in the target image on the attitude of the target object, and the factors considered are relatively comprehensive, so the attitude of the target object obtained based on this method has high accuracy, and further has It is beneficial to improve the accuracy of the acquired motion information of the target image.
  • the training module 1604 is configured to perform the training on the model to be trained based on the predicted projection result of the predicted pose of the target object on the image to be processed and the real projection result of the real pose of the target object on the image to be processed. Training to get the gesture recognition model.
  • the location information includes the coordinates of the pixel points in the image coordinate system, and the image coordinate system is constructed based on the image to be processed.
  • the predicted pose of the target object includes the predicted orientation of the target object in the camera coordinate system and the predicted body behavior of the target object in the camera coordinate system, and the camera coordinate system is constructed based on the camera that captures the image to be processed.
  • the processing module 1602 is also used to process the target area of the image to be processed and the position information of the pixels in the image to be processed through the pose recognition model to obtain shape parameters and displacement parameters, pose parameters, shape The parameters and the displacement parameters are used together to obtain the pose of the target object.
  • the device further includes: a normalization module, configured to perform normalization processing on the position information of pixels in the image to be processed to obtain normalized position information; the processing module 1602 uses The attitude parameter is obtained by processing the image to be processed and the normalized position information through the model to be trained.
  • a normalization module configured to perform normalization processing on the position information of pixels in the image to be processed to obtain normalized position information
  • the processing module 1602 uses The attitude parameter is obtained by processing the image to be processed and the normalized position information through the model to be trained.
  • the target object is a human body.
  • FIG. 17 is a schematic structural diagram of the execution device provided in the embodiment of the present application.
  • the execution device 1700 may specifically be a mobile phone, a tablet, a notebook computer, a smart wearable device, a server, etc., which is not limited here.
  • the image classification apparatus described in the embodiment corresponding to FIG. 13 or FIG. 14 may be deployed on the execution device 1700 to realize the gesture recognition function in the embodiment corresponding to FIG. 6 or FIG. 8 .
  • the execution device 1700 includes: a receiver 1701, a transmitter 1702, a processor 1703, and a memory 1704 (the number of processors 1703 in the execution device 1700 may be one or more, and one processor is taken as an example in FIG. 17 ) , where the processor 1703 may include an application processor 17031 and a communication processor 17032 .
  • the receiver 1701 , the transmitter 1702 , the processor 1703 and the memory 1704 may be connected through a bus or in other ways.
  • the memory 1704 may include read-only memory and random-access memory, and provides instructions and data to the processor 1703 .
  • a part of the memory 1704 may also include a non-volatile random access memory (non-volatile random access memory, NVRAM).
  • NVRAM non-volatile random access memory
  • the memory 1704 stores processors and operating instructions, executable modules or data structures, or their subsets, or their extended sets, wherein the operating instructions may include various operating instructions for implementing various operations.
  • the processor 1703 controls the operations of the execution device.
  • various components of the execution device are coupled together through a bus system, where the bus system may include not only a data bus, but also a power bus, a control bus, and a status signal bus.
  • the various buses are referred to as bus systems in the figures.
  • the methods disclosed in the foregoing embodiments of the present application may be applied to the processor 1703 or implemented by the processor 1703 .
  • the processor 1703 may be an integrated circuit chip, which has a signal processing capability. In the implementation process, each step of the above method may be completed by an integrated logic circuit of hardware in the processor 1703 or instructions in the form of software.
  • the above-mentioned processor 1703 may be a general-purpose processor, a digital signal processor (digital signal processing, DSP), a microprocessor or a microcontroller, and may further include an application specific integrated circuit (ASIC), field programmable Field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • DSP digital signal processing
  • FPGA field programmable Field-programmable gate array
  • the processor 1703 may implement or execute various methods, steps, and logic block diagrams disclosed in the embodiments of the present application.
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • the steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor.
  • the software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register.
  • the storage medium is located in the memory 1704, and the processor 1703 reads the information in the memory 1704, and completes the steps of the above method in combination with its hardware.
  • the receiver 1701 can be used to receive input digital or character information, and generate signal input related to performing device related settings and function control.
  • the transmitter 1702 can be used to output digital or character information through the first interface; the transmitter 1702 can also be used to send instructions to the disk group through the first interface to modify the data in the disk group; the transmitter 1702 can also include a display device such as a display screen .
  • the processor 1703 is configured to perform gesture recognition on the image by using the gesture recognition model in the embodiment corresponding to FIG. 6 or FIG. 8 .
  • FIG. 18 is a schematic structural diagram of the training device provided in the embodiment of the present application.
  • the training device 1800 is implemented by one or more servers, and the training device 1800 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (central processing units, CPU) 1814 (eg, one or more processors) and memory 1832, one or more storage media 1830 (eg, one or more mass storage devices) for storing application programs 1842 or data 1844.
  • the memory 1832 and the storage medium 1830 may be temporary storage or persistent storage.
  • the program stored in the storage medium 1830 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the training device. Furthermore, the central processing unit 1814 may be configured to communicate with the storage medium 1830 , and execute a series of instruction operations in the storage medium 1830 on the training device 1800 .
  • the training device 1800 can also include one or more power supplies 1826, one or more wired or wireless network interfaces 1850, one or more input and output interfaces 1858; or, one or more operating systems 1841, such as Windows ServerTM, Mac OS XTM , UnixTM, LinuxTM, FreeBSDTM and so on.
  • operating systems 1841 such as Windows ServerTM, Mac OS XTM , UnixTM, LinuxTM, FreeBSDTM and so on.
  • the training device may execute the model training method in the embodiment corresponding to FIG. 10 or FIG. 12 .
  • the embodiment of the present application also relates to a computer storage medium, where a program for signal processing is stored in the computer-readable storage medium, and when the program is run on the computer, the computer executes the steps performed by the aforementioned execution device, or, The computer is caused to perform the steps as performed by the aforementioned training device.
  • the embodiment of the present application also relates to a computer program product, where instructions are stored in the computer program product, and when executed by a computer, the instructions cause the computer to perform the steps performed by the aforementioned executing device, or cause the computer to perform the steps performed by the aforementioned training device.
  • the execution device, training device or terminal device provided in the embodiment of the present application may specifically be a chip.
  • the chip includes: a processing unit and a communication unit.
  • the processing unit may be, for example, a processor, and the communication unit may be, for example, an input/output interface, pins or circuits etc.
  • the processing unit can execute the computer-executed instructions stored in the storage unit, so that the chips in the execution device execute the data processing methods described in the above embodiments, or make the chips in the training device execute the data processing methods described in the above embodiments.
  • the storage unit is a storage unit in the chip, such as a register, a cache, etc.
  • the storage unit may also be a storage unit located outside the chip in the wireless access device, such as only Read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (random access memory, RAM), etc.
  • ROM Read-only memory
  • RAM random access memory
  • FIG. 19 is a schematic structural diagram of a chip provided by the embodiment of the present application.
  • the chip can be represented as a neural network processor NPU 1900, and the NPU 1900 is mounted to the main CPU (Host CPU) as a coprocessor ), the tasks are assigned by the Host CPU.
  • the core part of the NPU is the operation circuit 1903, and the operation circuit 1903 is controlled by the controller 1904 to extract matrix data in the memory and perform multiplication operations.
  • the operation circuit 1903 includes multiple processing units (Process Engine, PE).
  • arithmetic circuit 1903 is a two-dimensional systolic array.
  • the arithmetic circuit 1903 may also be a one-dimensional systolic array or other electronic circuits capable of performing mathematical operations such as multiplication and addition.
  • arithmetic circuit 1903 is a general-purpose matrix processor.
  • the operation circuit fetches the data corresponding to the matrix B from the weight memory 1902, and caches it in each PE in the operation circuit.
  • the operation circuit takes the data of matrix A from the input memory 1901 and performs matrix operation with matrix B, and the obtained partial or final results of the matrix are stored in the accumulator (accumulator) 1908 .
  • the unified memory 1906 is used to store input data and output data.
  • the weight data directly accesses the controller (Direct Memory Access Controller, DMAC) 1905 through the storage unit, and the DMAC is transferred to the weight storage 1902.
  • Input data is also transferred to unified memory 1906 by DMAC.
  • DMAC Direct Memory Access Controller
  • the BIU is the Bus Interface Unit, that is, the bus interface unit 1913, which is used for the interaction between the AXI bus and the DMAC and the instruction fetch buffer (Instruction Fetch Buffer, IFB) 1909.
  • IFB Instruction Fetch Buffer
  • the bus interface unit 1913 (Bus Interface Unit, BIU for short), is used for the instruction fetch memory 1909 to obtain instructions from the external memory, and is also used for the storage unit access controller 1905 to obtain the original data of the input matrix A or the weight matrix B from the external memory.
  • BIU Bus Interface Unit
  • the DMAC is mainly used to move the input data in the external memory DDR to the unified memory 1906 , move the weight data to the weight memory 1902 , or move the input data to the input memory 1901 .
  • the vector computing unit 1907 includes a plurality of computing processing units, and if necessary, further processes the output of the computing circuit 1903, such as vector multiplication, vector addition, exponent operation, logarithmic operation, size comparison and so on. It is mainly used for non-convolutional/fully connected layer network calculations in neural networks, such as Batch Normalization (batch normalization), pixel-level summation, upsampling of predicted label planes, etc.
  • the vector computation unit 1907 can store the vector of the processed output to unified memory 1906 .
  • the vector calculation unit 1907 can apply a linear function; or, a non-linear function to the output of the operation circuit 1903, such as performing linear interpolation on the predicted label plane extracted by the convolutional layer, and then for example, a vector of accumulated values to generate an activation value .
  • the vector calculation unit 1907 generates normalized values, pixel-level summed values, or both.
  • the vector of processed outputs can be used as an activation input to operational circuitry 1903, for example for use in subsequent layers in a neural network.
  • An instruction fetch buffer (instruction fetch buffer) 1909 connected to the controller 1904 is used to store instructions used by the controller 1904;
  • the unified memory 1906, the input memory 1901, the weight memory 1902 and the fetch memory 1909 are all On-Chip memories. External memory is private to the NPU hardware architecture.
  • the processor mentioned above can be a general-purpose central processing unit, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of the above-mentioned programs.
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, and the components shown as units may or may not be A physical unit can be located in one place, or it can be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • the connection relationship between the modules indicates that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines.
  • the essence of the technical solution of this application or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a readable storage medium, such as a floppy disk of a computer , U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., including several instructions to make a computer device (which can be a personal computer, training device, or network device, etc.) execute the instructions described in various embodiments of the present application method.
  • a computer device which can be a personal computer, training device, or network device, etc.
  • all or part of them may be implemented by software, hardware, firmware or any combination thereof.
  • software When implemented using software, it may be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer instructions.
  • the computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices.
  • the computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transferred from a website, computer, training device, or data
  • the center transmits to another website site, computer, training device or data center via wired (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.).
  • wired eg, coaxial cable, fiber optic, digital subscriber line (DSL)
  • wireless eg, infrared, wireless, microwave, etc.
  • the computer-readable storage medium may be any available medium that can be stored by a computer, or a data storage device such as a training device or a data center integrated with one or more available media.
  • the available medium may be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (Solid State Disk, SSD)), etc.

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Abstract

La présente invention concerne un procédé de reconnaissance de posture et un dispositif associé. La posture d'un objet cible, qui est obtenue après le traitement d'une image comprenant l'objet cible, présente une précision relativement élevée, facilitant ainsi l'amélioration de la précision de l'information de mouvement d'une image cible acquise. Le procédé selon la présente invention comprend les étapes suivantes: l'acquisition d'une image cible; et le traitement, au moyen d'un modèle de reconnaissance de posture, d'une zone cible de l'image cible et de l'information de position de la zone cible dans l'image cible, afin d'obtenir un paramètre de posture, la zone cible étant la zone où se trouve l'objet cible, et le paramètre de posture étant utilisé pour l'acquisition de la posture de l'objet cible.
PCT/CN2022/128504 2021-11-15 2022-10-31 Procédé de reconnaissance de posture et dispositif associé WO2023083030A1 (fr)

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CN114241597A (zh) * 2021-11-15 2022-03-25 华为技术有限公司 一种姿态识别方法及其相关设备
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CN116912951B (zh) * 2023-09-13 2023-12-22 华南理工大学 人体姿态的评估方法及装置

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US20190205668A1 (en) * 2017-11-22 2019-07-04 Kabushiki Kaisha Toshiba Object detecting apparatus, object detecting method, and computer program product
CN112446917A (zh) * 2019-09-03 2021-03-05 北京地平线机器人技术研发有限公司 一种姿态确定方法及装置
CN112668549A (zh) * 2021-01-15 2021-04-16 北京格灵深瞳信息技术股份有限公司 行人姿态分析方法、系统、及终端、存储介质
CN114241597A (zh) * 2021-11-15 2022-03-25 华为技术有限公司 一种姿态识别方法及其相关设备

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US20190205668A1 (en) * 2017-11-22 2019-07-04 Kabushiki Kaisha Toshiba Object detecting apparatus, object detecting method, and computer program product
CN112446917A (zh) * 2019-09-03 2021-03-05 北京地平线机器人技术研发有限公司 一种姿态确定方法及装置
CN112668549A (zh) * 2021-01-15 2021-04-16 北京格灵深瞳信息技术股份有限公司 行人姿态分析方法、系统、及终端、存储介质
CN114241597A (zh) * 2021-11-15 2022-03-25 华为技术有限公司 一种姿态识别方法及其相关设备

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