Abnormal crowd behavior detection and localization using maximum sub-sequence search
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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We propose a novel algorithm for video event detection and localization as the optimal path discovery problem in spatio-temporal video space. By finding the optimal spatio-temporal path, our method not only detects the starting and ending points of the event, but also accurately locates it in each video frame. Moreover, our method is robust to the scale and intra-class variations of the event, as well as false and missed local detections, therefore improves the overall detection and localization accuracy. The proposed search algorithm obtains the global optimal solution with proven lowest computational complexity. Experiments on realistic video datasets demonstrate that our proposed method can be applied to different types of event detection tasks, such as abnormal event detection and walking pedestrian detection.