Detection of Abnormal Behavior by Scene Analysis in Surveillance Video 


Vol. 36,  No. 12, pp. 744-752, Dec.  2011


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  Abstract

In intelligent surveillance system, various methods for detecting abnormal behavior were proposed recently. However, most researches are not robust enough to be utilized for actual reality which often has occlusions because of assumption the researches have that individual objects can be tracked. This paper presents a novel method to detect abnormal behavior by analysing major motion of the scene for complex environment in which object tracking cannot work. First, we generate Visual Word and Visual Document from motion information extracted from input video and process them through LDA(Latent Dirichlet Allocation) algorithm which is one of document analysis technique to obtain major motion information(location, magnitude, direction, distribution) of the scene. Using acquired information, we compare similarity between motion appeared in input video and analysed major motion in order to detect motions which does not match to major motions as abnormal behavior.

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  Cite this article

[IEEE Style]

G. Bae, Y. Uh, S. Kwak, H. Byun, "Detection of Abnormal Behavior by Scene Analysis in Surveillance Video," The Journal of Korean Institute of Communications and Information Sciences, vol. 36, no. 12, pp. 744-752, 2011. DOI: .

[ACM Style]

Guntae Bae, Youngjung Uh, Sooyeong Kwak, and Hyeran Byun. 2011. Detection of Abnormal Behavior by Scene Analysis in Surveillance Video. The Journal of Korean Institute of Communications and Information Sciences, 36, 12, (2011), 744-752. DOI: .

[KICS Style]

Guntae Bae, Youngjung Uh, Sooyeong Kwak, Hyeran Byun, "Detection of Abnormal Behavior by Scene Analysis in Surveillance Video," The Journal of Korean Institute of Communications and Information Sciences, vol. 36, no. 12, pp. 744-752, 12. 2011.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880