A Human Behavior Recognition Method Based on Depth Video Sequence

Abstract

The invention discloses a human behavior recognition method based on a depth video sequence. The method calculates the four-dimensional normal vectors of all pixels in the video sequence, and extracts pixels in different layers by constructing a time-space pyramid model of the behavior sequence in different time-space domains. Based on the low-level features, the group sparse dictionary is learned based on the low-level features, and the sparse coding of the low-level features is obtained. The spatial average pooling and temporal maximum pooling are used to integrate the coding, so as to obtain high-level features as the descriptor of the final behavior sequence. This kind of descriptor can effectively preserve the spatiotemporal multi-resolution information of human behavior, and at the same time, by eliminating the similar content contained in different behavior categories, a sparse dictionary with stronger expressive power can be obtained to effectively improve the behavior recognition rate.

Description

technical field [0001] The invention belongs to the technical field of computer pattern recognition, and in particular relates to a human behavior recognition method based on depth video sequences. Background technique [0002] Vision is an important way for human beings to observe and understand the world. With the continuous improvement of computer processing capabilities, we hope that computers can have part of the visual functions of human beings, helping or even replacing human eyes and brains to observe and perceive external things. With the improvement of computer hardware processing ability and the emergence of computer vision technology, people's expectation of computer may become a reality. Human behavior recognition has always been a research hotspot in the fields of pattern recognition, computer vision, and artificial intelligence. The purpose of video-based human behavior recognition is to understand and recognize individual human actions, interactive movement...

AI Technical Summary

Problems solved by technology

[0006] The existing human behavior recognition technology based on the normal vector is the most popular. The human behavior recognition method based on the normal vector currently has the following two problems: (1) When using the normal vector to construct the descriptor, since the extraction is based on a certain point The information in the spatial-temporal neighborhood of the layer cannot more accurately represent the detailed information of the local descriptor; (2) when classifying the video sequence of the behavior to be recognized, the atoms in the complete dictionary are used to represent the video sequence to be recognized, if different behaviors The video sequence has similar features, then the similar features are also partially represented, and the incorrectly obtained representation coefficient will reduce the recognition rate for classification

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