Detection of Human Behavior Anomaly Based on the Optical Flow Co-occurrence Matrix
[1]Zeng Qingshan,Song Qingxiang,Fan Mingli.Detection of Human Behavior Anomaly Based on the Optical Flow Co-occurrence Matrix[J].Journal of Zhengzhou University (Engineering Science),2018,39(03):29-33.[doi:10.13705/j.issn.1671-6833.2017.06.032]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
39
Number of periods:
2018 03
Page number:
29-33
Column:
Public date:
2018-05-10
- Title:
- Detection of Human Behavior Anomaly Based on the Optical Flow Co-occurrence Matrix
- Keywords:
- crowd behavior anomaly detection; optical flow; optical flow co-occurrence matrix; support vector machine
- CLC:
- -
- Abstract:
- The traditional anomaly detection algorithm for human beharior that based on image texture featurestended to describe the change of human image texture ranther than the actual situationof human motion behavior. Its detection porformance was not so good. In this paper, a method of feature extraxtion was proposed to reflect the real situation of human motion behavior. Firstly, the optical flow information was extracted by Lucas-Kanade optical flow algorithm, and the co-occurrence matrix and optical flow direction co-occurrence matrix were established. Then, the characteristics of two order distance,contrast, entropy and similarity are extraced by the co-occurrence matrix, and then combined them with the mean value of optical flow to form a feature vector to train the support vector machine(SVM). Finally, this algorithm was used to determine whether the crowd had abnormal behavior. The simulation results showed that the feature extraction method in this paper had more in depth processing of the croed motion information provided by the optical flow method.Compared with the mainstream algorithm, it has a better recognition performance.
- References:
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Last Update:
2018-05-03