Body posture recognition method and device based on LSTM and storage medium

Abstract

The invention relates to the technical field of biological recognition, and provides a body posture recognition method based on LSTM. The body posture recognition method comprises the steps: obtainingan action video of a to-be-recognized main body; extracting action feature information in the acquired action video of the to-be-identified main body through OpenPose, wherein the action feature information at least comprises skeleton key point information; and recognizing an action specification degree corresponding to the action feature information according to the action feature information and a pre-trained and generated body posture recognition model, wherein the body posture recognition model is a target neural network model generated according to a preset standard action, and the target neural network model is generated by training according to standard action feature information arranged according to a time sequence. According to the body posture recognition method, video actionsdo not need to be cut into isolated features to be recognized, and learning recognition is carried out through cooperation with the neural network model, and the body posture recognition process is rapid and accurate, and the user experience is improved.

Description

technical field [0001] The present invention relates to the technical field of biometrics, in particular to an LSTM-based body gesture recognition method, device, and computer-readable storage medium. Background technique [0002] Many activities, especially competitive sports, such as swimming, playing table tennis, diving, gymnastics, etc., have specific requirements for every subtle movement during training and competition, in order to achieve better training or competition effects. In the process of physical training, many movement adjustments and error corrections are mostly completed by special coaches in the process of professional guidance, and it is difficult for athletes to find their own movement errors during exercise. In the existing physical competitions, such as aerobics competitions, radio gymnastics competitions, and dance competitions, referees currently score individual performances. Although the referees are professionals, there are also misjudgments, mis...

AI Technical Summary

Problems solved by technology

However, both methods have their own shortcomings. The RGB image contains too much information, which is not conducive to the extraction of gesture features.

However, in the depth image, it is easy for the limbs to occlude each other, which affects the recognition accuracy.

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