Abnormal events detection in crowded scenes by trajectory cluster

Shifu Zhou; Zhijiang Zhang; Dan Zeng; Wei Shen

doi:10.1117/12.2180725

6 March 2015 Abnormal events detection in crowded scenes by trajectory cluster

Shifu Zhou, Zhijiang Zhang, Dan Zeng, Wei Shen

Proceedings Volume 9446, Ninth International Symposium on Precision Engineering Measurement and Instrumentation; 944614 (2015) https://doi.org/10.1117/12.2180725
Event: International Symposium on Precision Engineering Measurement and Instrumentation, 2014, Changsha/Zhangjiajie, China

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.

Citation Download Citation

Shifu Zhou, Zhijiang Zhang, Dan Zeng, and Wei Shen "Abnormal events detection in crowded scenes by trajectory cluster", Proc. SPIE 9446, Ninth International Symposium on Precision Engineering Measurement and Instrumentation, 944614 (6 March 2015); https://doi.org/10.1117/12.2180725

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