Human posture recognition methods, devices, computer equipment, and storage media

By constructing a human pose recognition model based on a deep convolutional neural network, the problem of limited computing and storage resources was solved, and the accuracy and efficiency of human pose recognition in complex backgrounds were improved.

CN118334744BActive Publication Date: 2026-05-26SOUTH CHINA NORMAL UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SOUTH CHINA NORMAL UNIV
Filing Date
2024-04-24
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing human pose recognition models in complex backgrounds are limited by computational and storage resources, making it difficult to perform effective and accurate human pose recognition, especially when there is occlusion or changes in lighting conditions, which increases the difficulty of recognition.

Method used

By constructing a human pose recognition model, iterative training is performed using a deep convolutional neural network, including a downsampling network, a backbone network, an attention extraction network, a deep feature extraction network, and a joint matching network. Deep convolutional feature maps are extracted, joint matching and training are performed, and information about the relevant regions for human pose estimation is adaptively captured.

Benefits of technology

Despite limited computing and storage resources, this technology improves the accuracy and performance of human pose recognition, enabling effective human pose recognition in complex contexts.

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Abstract

This invention relates to the field of human pose recognition, and particularly to a human pose recognition method, apparatus, computer device, and storage medium. By extracting deep convolutional feature maps from several sample human pose images as training data, a human pose recognition model is iteratively trained to construct a target human pose recognition model. While ensuring the accuracy of human pose recognition, the model can adaptively capture the importance of information related to human pose estimation regions, further improving the model's performance. This enables the model to perform effective and accurate human pose recognition even with limited computing and storage resources.
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