Kernel null-space-based abnormal event detection using hybrid motion information

Yanjiao Shi; Yugen Yi; Qing Zhang; Jiangyan Dai

doi:10.1117/1.JEI.28.2.021011

12 March 2019 Kernel null-space-based abnormal event detection using hybrid motion information

Yanjiao Shi, Yugen Yi, Qing Zhang, Jiangyan Dai

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), Natural Science Foundation of China, Natural Science Foundation of Jiangxi Province, Jiangxi Provincial Department of Education, Shanghai Institute of Technology

Journal of Electronic Imaging, Vol. 28, Issue 2, 021011 (March 2019). https://doi.org/10.1117/1.JEI.28.2.021011

Abstract

Abnormal event detection in crowded scenes is a challenging task in the computer vision community. A hybrid motion descriptor named the multiscale histogram of first- and second-order motion is proposed for abnormal event detection. The second-order motion describes the change in motion and is extracted by optical flow-based instantaneous tracking, which avoids object tracking in crowded scenes. For the modeling of normal events, a kernel null Foley–Sammon transform (KNFST) is introduced. KNFST makes a projection in the null space, where normal samples of all types are treated jointly instead of considering each known class individually. Experiments conducted on two benchmark datasets and comparisons to state-of-the-art methods demonstrate the superiority of the proposed method.

Citation Download Citation

Yanjiao Shi, Yugen Yi, Qing Zhang, and Jiangyan Dai "Kernel null-space-based abnormal event detection using hybrid motion information," Journal of Electronic Imaging 28(2), 021011 (12 March 2019). https://doi.org/10.1117/1.JEI.28.2.021011

Received: 6 September 2018; Accepted: 26 February 2019; Published: 12 March 2019

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