Multi-visual-angle action recognition method

Abstract

The invention discloses a multi-visual-angle action recognition method. The method includes the two processes of action training and action recognizing. In the action training process, a two-dimensional conditional random field method is used for training a classifier. The action recognition process includes the following steps that space-time interest points are extracted; feature descriptors are calculated; dimension reducing is performed on the feature descriptors; the feature descriptors are clustered to obtain a preprocessing file; the preprocessing file is sent into the classifier obtained in the training process. The space-time relation among the space-time interest points is sufficiently utilized, and features among different actions are effectively described; K-means clustering is adopted for clustering different actions into different categories, and therefore the distinction degree of action recognition is increased; through introducing the two-dimensional conditional random field, the time action sequence of a single camera and a space action sequence among multiple cameras are effectively modeled, so that a training model is more accurate, and then actions of the human body are effectively recognized.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a multi-view action recognition method. Background technique [0002] Using video cameras and computers to "see" instead of human eyes, that is, computer vision technology, is beginning to receive more and more attention. This technology can recognize images and videos and perform further processing by taking pictures with cameras and using preset algorithms in the computer. This technology attempts to establish an artificial intelligence system that obtains and processes information from images or videos. [0003] Moreover, with the increasing maturity of video monitoring technology and the popularization of monitoring equipment, the cost of monitoring equipment such as cameras is decreasing day by day, and the acquisition of video information becomes easier and more convenient, and the quality of video information is also getting higher and higher. Based on...

AI Technical Summary

Problems solved by technology

[0012] Because the existing technology has poor behavior recognition effect on subtle differences, is sensitive to changes in motion duration and noise, and adapts to observation sequences with dependencies, and cannot get good results for more complex motions, only one dimensional linear conditional random field, the present invention proposes a multi-view action recognition method

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