Abstract In trajectory-based abnormal event detection, abnormal trajectory usually has abnormality in some parts of the whole trajectory and the rest are normal. However, most of the previous approaches are not able to detect this kind of abnormality easily. Aiming at the problem, an approach is proposed for abnormal event detection based on trajectory segmentation within the framework of multi-instance learning. In the proposed method, every trajectory is segmented into independent sub-trajectories based on their curvature firstly. Then, the sub-trajectories are modeled by hierarchical Dirichlet process-hidden Markov model (HDP-HMM). Finally, within the multi-instance learning framework, the whole trajectory is considered as bags while normal ones are negative bags, abnormal ones are positive bags, and sub-trajectories are instances in the bags. Experimental results show the proposed method achieves higher precision and recall than traditional ones.
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2011, Vol. 24 
