AI-based motion posture recognition system

By using an AI-based motion posture recognition system, the temporal coordinates of human skeleton nodes are extracted, a temporal skeleton graph structure is constructed, and node trajectory features are analyzed. This solves the problem of insufficient recognition accuracy of traditional systems in complex environments and achieves efficient motion capture and posture analysis.

CN121564446AActive Publication Date: 2026-02-24NANTONG UNIV
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Patent Information

Application Number
CN202610084148.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-22
Publication Date
2026-02-24
Estimated Expiration
2046-01-22

AI Technical Summary

Technical Problem

Traditional motion posture recognition systems rely on sensors or specific devices with fixed layouts, which are inflexible, difficult to adapt to diverse scenarios, and lack recognition accuracy in complex environments, making it impossible to achieve high-frequency motion capture and fine posture analysis.

Method used

By using an AI-based motion posture recognition system, a multi-node temporal coordinate set of the human skeleton in video frames is extracted, a temporal skeleton graph structure sequence is constructed, node trajectory features are analyzed, and the action process is analyzed in segments to achieve accurate analysis of complex actions.

Benefits of technology

Without relying on external hardware, it achieves accurate parsing of complex movements, improving the accuracy and timeliness of posture recognition.

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Abstract

The invention relates to the technical field of action recognition, in particular to an AI-based motion posture recognition system, which comprises a key point extraction module, a graph structure construction module, a node trajectory modeling module, an action segmentation analysis module and a posture recognition output module. According to the method, the spatial position of each node of the human body in the continuous image under the time sequence structure is extracted, and the graph structure sequence based on the topological relation between the nodes and the motion trend is established, so that the dynamic modeling and the structure expression of the skeleton motion can be realized, and the trajectory characteristics of node position change, speed fluctuation, direction change and the like are analyzed; according to the method, key paragraphs with inversion and abnormity in actions are effectively identified, action segmentation classification is carried out in combination with frame sequence spacing and key node activity states, accurate analysis of complex action processes is realized under the condition of not depending on external hardware, and the accuracy and timeliness of posture identification are improved in a mode of matching with a standard template.
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