Abnormal Behavior Detection Based on Global Motion Orientation

DOI

10.2991/icsem.2013.89How to use a DOI?

Keywords

abnormal behavior detection, global motion orientation, support vector machine (SVM).

Abstract

A novel approach is introduced in this paper to detect abnormal behavior based on global motion orientation. Compare to the normal behavior (walking, shaking hands etc.), abnormal behavior has different orientation. The method we introduced divides each frame into blocks, makes statistical analysis of the global motion direction histogram of all frame blocks and extracts characteristics. At last, behavior is detected with support vector machine (SVM). Experiment shows that the method proposed in the paper has certain robustness and can achieve real-time monitoring.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xuyan Ma
AU  - Guomao Liang
AU  - Wei Yu
AU  - Zhiyi Qu
PY  - 2013/04
DA  - 2013/04
TI  - Abnormal Behavior Detection Based on Global Motion Orientation
BT  - Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM 2013)
PB  - Atlantis Press
SP  - 461
EP  - 464
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsem.2013.89
DO  - 10.2991/icsem.2013.89
ID  - Ma2013/04
ER  -