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A multi-person human pose estimation method

A human body posture and human body technology, applied in the field of multi-person human body posture estimation, can solve the problems of low accuracy rate of key point detection of human body, poor robustness of occlusion interference, etc., and achieve the effect of improving recognition rate and avoiding interference

Active Publication Date: 2022-02-08
HEBEI UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0016] The technical problem to be solved by the present invention is to provide a method for estimating the pose of a multi-person human body, which is a method for estimating the pose of a multi-person human body based on depth features. The key point detection model uses a two-stage training method combining bottom-up and top-down methods to train the deep feature human key point detection model, and finally uses the two-stage trained deep feature human key point detection model to detect human key points. And the redundant key points that do not belong to the target person are removed through the clustering process of human body key points, and then the result of multi-person human body pose estimation is output, which overcomes the problem of the existing multi-person human body pose estimation technology in the case of dense crowds. Poor robustness to human body occlusion interference, and low accuracy rate of human body key point detection

Method used

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  • A multi-person human pose estimation method
  • A multi-person human pose estimation method
  • A multi-person human pose estimation method

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Embodiment

[0116] In this embodiment, a method for estimating the posture of a multi-person human body, the specific steps are as follows:

[0117] A process. Establishment and training of deep feature human key point detection model:

[0118] The first step, image data preprocessing:

[0119] Step (1.1), image normalization:

[0120] Obtain MScoco image data set for multi-person human pose estimation. This data set is a public data set in the field of human pose estimation. It is divided into label files and image files. The label file saves the location information of 17 human key points that have been marked for each image. , to preprocess the multi-person human body pose estimation images in the acquired multi-person human body pose estimation MScoco image data set, that is, to adjust the image to a size of 384×288 pixels, and then according to the three combinations of expectation and standard deviation: (0.485,0.229 ), (0.456, 0.244), (0.406, 0.255), respectively standardize the ...

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Abstract

The present invention relates to a method for estimating the posture of a multi-person human body, which relates to the processing of a record carrier for recognizing graphics, and is a method for estimating the posture of a multi-person human body based on depth features. The deep feature human key point detection model uses a two-stage training method combining bottom-up and top-down methods to train the deep feature human key point detection model, and finally uses the two-stage trained deep feature human key point detection model to detect the human body key points, and remove redundant key points that do not belong to the target person through human body key point clustering processing, and then output the result of multi-person human body pose estimation, which overcomes the problem of existing multi-person human body pose estimation technology in the case of dense crowds , poor robustness to target human body occlusion interference, and low accuracy rate of human body key point detection.

Description

technical field [0001] The technical solution of the present invention relates to the processing of a record carrier for recognizing graphics, in particular to a method for estimating the pose of a multi-person human body. Background technique [0002] Human body posture estimation technology is very important to describe the trajectory of human motion and predict human behavior. This technology is used to locate the motion trajectory of key points describing the position of human joints and record its motion data, realize 3D animation to simulate human motion to make movies and TV, and pass The recorded human motion trajectory and data are used to analyze human motion, which is applied to the classification of human motion, the detection of abnormal human behavior, and the field of automatic driving. [0003] The existing human pose estimation methods are divided into machine learning methods and deep learning methods according to the different feature definition methods. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V40/10G06V10/774G06V10/762G06V10/766G06V10/82G06K9/62G06T7/73
CPCG06T7/73G06T2207/20081G06T2207/20084G06T2207/30196G06V40/10G06V2201/07G06F18/23G06F18/214
Inventor 于明金宇于洋郭迎春阎刚郝小可师硕朱叶刘依
Owner HEBEI UNIV OF TECH
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