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Proceedings Paper

Abnormal events detection in crowded scenes by trajectory cluster
Author(s): Shifu Zhou; Zhijiang Zhang; Dan Zeng; Wei Shen
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Paper Abstract

Abnormal events detection in crowded scenes has been a challenge due to volatility of the definitions for both normality and abnormality, the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. A novel framework was proposed for the detection of unusual events in crowded scenes using trajectories produced by moving pedestrians based on an intuition that the motion patterns of usual behaviors are similar to these of group activity, whereas unusual behaviors are not. First, spectral clustering is used to group trajectories with similar spatial patterns. Different trajectory clusters represent different activities. Then, unusual trajectories can be detected using these patterns. Furthermore, behavior of a mobile pedestrian can be defined by comparing its direction with these patterns, such as moving in the opposite direction of the group or traversing the group. Experimental results indicated that the proposed algorithm could be used to reliably locate the abnormal events in crowded scenes.

Paper Details

Date Published: 6 March 2015
PDF: 7 pages
Proc. SPIE 9446, Ninth International Symposium on Precision Engineering Measurement and Instrumentation, 944614 (6 March 2015); doi: 10.1117/12.2180725
Show Author Affiliations
Shifu Zhou, Shanghai Univ. (China)
Zhijiang Zhang, Shanghai Univ. (China)
Dan Zeng, Shanghai Univ. (China)
Wei Shen, Shanghai Univ. (China)


Published in SPIE Proceedings Vol. 9446:
Ninth International Symposium on Precision Engineering Measurement and Instrumentation
Junning Cui; Jiubin Tan; Xianfang Wen, Editor(s)

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