Abnormal Behavior Detection Based on Global Motion Orientation

Xu Yan Ma; Guo Mao Liang; Wei  Yu; Zhi Yi  Qu

doi:10.4028/www.scientific.net/AMR.765-767.2264

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 765-767Abnormal Behavior Detection Based on Global Motion...

Abnormal Behavior Detection Based on Global Motion Orientation

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.

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Periodical:

Advanced Materials Research (Volumes 765-767)

Pages:

2264-2267

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.765-767.2264

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Online since:

September 2013

Authors:

Xu Yan Ma, Guo Mao Liang, Wei Yu, Zhi Yi Qu

Keywords:

Abnormal Behavior Detection, Global Motion Orientation, Support Vector Machine (SVM)

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References

[1] HU Zhi-Lan, JIANG Fan: Anomaly Detection Based on Motion Direction, ACTA AUTOMATICA SINICA, 2008, 34(11): 1348-1357.

DOI: 10.3724/sp.j.1004.2008.01348

Google Scholar

[2] Gary R. Bradski, James Davis: Motion Segmentation and Pose Recognition with Motion History Gradients, Machine Vision and Application, 2002, 13(7): 174-184.

DOI: 10.1007/s001380100064

Google Scholar

[3] Kyoko SUDO, Tatsuya Osawa, Detecting the degree of anomaly in security video sequence, MVA2007 IAPR Conference on Machine Vision Applications, Tokyo, Japan: (2007).

Google Scholar

[4] Bian Zhao-Qi, Zhang Xue-Gong, Pattern Recognition. Beijing: Tsinghua University Press (2002).

Google Scholar

[5] The video sequence come from the EC Funded CAVIAR project/IST 2001 37540, found at URL: http: /homepages. inf. ed. ac. uk/rbf/CAVIAR.

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