Video human behavior recognition method based on significant trajectory and time-space evolution information

Abstract

The present invention provides a video human behavior recognition method based on a significant trajectory and time-space evolution information. According to the method, the optical flow information in a video is fully utilized, on the basis of improving a dense trajectory, through defining the static significance and dynamic significance of the trajectory, with a linear fusion mode, the combined significance of the trajectory is obtained through calculation, thus a background movement trajectory is effectively removed, and a foreground movement trajectory is extracted. For a problem that the rich middle and high level semantic information in a behavior video is ignored by a traditional representation method based on a low-level visual characteristic, the invention provides middle level visual characteristic expression which is a trajectory beam, human body behavior time-space evolution information is extracted to be a video characteristic expression, the background trajectory is removed effectively, the foreground movement trajectory is extracted, and the recognition effect of an algorithm is improved significantly.

Description

technical field [0001] The invention relates to the technical field of computer vision, and more specifically, to a video human behavior recognition method based on salient trajectory and spatio-temporal evolution information. Background technique [0002] With the development of the Internet and multimedia, video has become the main way for people to obtain information. Video-based human behavior recognition technology has been widely used in a series of scenarios such as intelligent video surveillance, video retrieval, virtual reality, and human interaction. In recent years, a large number of human behavior recognition methods from surveillance scenes to natural scenes have emerged, and the recognition accuracy of various public data sets is also constantly improving. However, the complexity of video motion in natural scenes (such as camera motion) leads to serious optical flow deviation, the inaccurate human positioning algorithm leads to mixed foreground and background m...

AI Technical Summary

Problems solved by technology

In addition, representation methods based on low-level visual features have achieved good results in human behavior recognition in surveillance scenes, but such methods usually ignore the rich mid-level and high-level semantic information in behavioral videos.

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