Human Abnormal Behavior Detection Based on RGBD Video's Skeleton Information Entropy

摘要

Conventional human abnormal behavior detection is mostly done in videos taken by visible-light cameras, and it is usually designed for a certain task. In order to solve the human abnormal behavior detection problem in general situation, this paper proposes a detection algorithm based on skeleton information entropy, by using the information from RGBD videos. In this paper, we assume that abnormal behavior is disordered. To sample the accurate features of human, we use RGBD cameras to get the skeleton information. Then, we analyze the information entropy of the angles of the skeleton, and find that the values of the information entropy are significantly higher in abnormal videos than in normal videos. The methods are tested in our database taken by Kinect in our lab and we present superior results whose recall is 92 % and precision is 95.83 %, and accuracy is 94 %.