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Unusual event detection and prediction based on sectional contextual edit distance

[+] Author Affiliations
Yi Zhang

Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, P.O. Box A0603221, 800 Dongchuan Road, Shanghai 200240, China

Jie Yang

Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, P.O. Box A0603221, 800 Dongchuan Road, Shanghai 200240, China

Kun Liu

Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, P.O. Box A0603221, 800 Dongchuan Road, Shanghai 200240, China

J. Electron. Imaging. 19(1), 013009 (March 05, 2010). doi:10.1117/1.3327951
History: Received June 02, 2009; Revised January 01, 2010; Accepted January 12, 2010; Published March 05, 2010; Online March 05, 2010
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We redefine the unusual event detection problem from a different point of view. Several fundamental event features are investigated and adopted. These features are redescribed in a uniform model. Thus, using this model, supervised/unsupervised unusual event detection algorithms can be designed to fit various situations. Trajectory is treated as the most important feature. To more accurately measure the similarity of different moving object trajectories, a novel distance measurement, the sectional contextual edit distance (SCED), is developed. In the SCED, cost functions are designed according to contextual information and trajectories are segmented into subsections automatically, based on the relevant contexts. Velocity and orientation are also taken into account in cost functions to build an integrated distance similarity measurement. Experimental results demonstrate better performance using the newly proposed similarity measurement while being compared with the existing methods, and some cases of the unusual event detection problem are also demonstrated.

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Citation

Yi Zhang ; Jie Yang and Kun Liu
"Unusual event detection and prediction based on sectional contextual edit distance", J. Electron. Imaging. 19(1), 013009 (March 05, 2010). ; http://dx.doi.org/10.1117/1.3327951


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