Behavior detection method

Abstract

The invention provides a behavior detection method. The behavior detection method comprises: extracting human body feature point coordinate information; enhancing the coordinate information content of the human body feature points to obtain kinematics information of human body postures; combining the human body feature point coordinate information and the kinematics information to obtain high-dimension information, and meanwhile, preseting a nonlinear dimension reduction algorithm to obtain low-dimension effective data subjected to redundancy elimination and low signal-to-noise ratio; and performing unsupervised clustering on the low-dimensional effective data, constructing convolutional neural network training to form a behavior recognition classifier, and outputting and displaying the motion behavior of the human body posture. After the attitude mode is obtained by fusing the relevance information of the attitude and the action and kinematics parameters and adopting an unsupervised algorithm, classifier training is carried out by using 1DCNN (one-dimensional convolutional neural network) and combining continuous frame time sequence information to obtain the action category, so that the effect of behavior recognition is achieved, and the problem that accurate behavior detection is relatively difficult is solved.

Description

Technical field [0001] The present invention relates to the field of human gesture detection and recognition technology, in particular to a method for detecting behavior. Background technique [0002] Currently precise behavior detection is still a difficult target. Recent advances in machine learning makes it possible to locate the limb, however, despite the physical position or posture can provide information, but the corresponding behavior interpretability quite low. Extraction behavior information needed to determine the spatial and temporal patterns of these locations. [0003] Accordingly, the present invention related information and kinematic parameters of the attitude and the fusion operation, after using unsupervised algorithm gesture mode, 1DCNN (one-dimensional convolutional neural network) and the frame timing information continuously combined classifier training operation to give category, in order to achieve the effect of behavior recognition, so as to solve the pr...

AI Technical Summary

Problems solved by technology

[0002] Accurate behavior detection is still a difficult goal

Recent advances in machine learning have enabled limb localization, however, although informative, the corresponding behavioral interpretability of limb position or pose is rather low

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