[1]XU Zhitong,LUO Yanmin,LIU Peizhong.Abnormal behavior detection of joint weighted reconstruction trajectory and histogram entropy[J].CAAI Transactions on Intelligent Systems,2018,13(6):1015-1026.[doi:10.11992/tis.201706070]
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Abnormal behavior detection of joint weighted reconstruction trajectory and histogram entropy

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 6 Page number: 1015-1026 Column: 学术论文—机器学习 Public date: 2018-10-25
Title:
Abnormal behavior detection of joint weighted reconstruction trajectory and histogram entropy
Author(s):
XU Zhitong1; 2;  LUO Yanmin1; 2;  LIU Peizhong3
1. College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China;
2. Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen 361021, China;
3. College of Engineering, Huaqiao University, Quanzhou 362021, China
Keywords:
abnormal behavior detection; jointly weighted; reconstruction trajectory; histogram entropy; Kalman filters; HOG features; spatio-temporal context; behavior directory
CLC:
TP391.41
DOI:
10.11992/tis.201706070
Abstract:
To solve the problem of low precision in detecting multi-target abnormal behaviors such as fighting and robbery, in this paper, a novel human abnormal behavior detection algorithm is proposed based on a combination of the weighted reconstruction trajectory and histogram entropy. First, the background subtraction method is combined with aspect ratio to extract pedestrian targets, and then Kalman filters and histogram of oriented gradient (HOG) features are integrated into a spatio-temporal context algorithm to realize a robust tracking of completely occluded pedestrians in a short time, and finally the tracked trajectories are trained; a normal behavior directory is constructed for the motion trajectories defined as normal, and the trajectories to be tested are sparsely reconstructed. An effective detection of abnormal behavior is realized by combining the weighted minimum reconstruction residuals and amplitude direction histogram entropy. A comparative experiment shows that the algorithm can effectively detect abnormal behaviors such as fighting and robbery, and the detection rate can exceed 92% under static background without occlusions.
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