An efficient subsequence search for video anomaly detection and localization

This paper presents a novel framework for anomaly event detection and localization in crowded scenes. For anomaly detection, one-class support vector machine with Bayesian derivation is applied to detect unusual events. We also propose a novel event representation, called subsequence, which refers to a time series of spatial windows in proximity. Unlike recent works encoded an event with a 3D bounding box which may contain irrelevant information, e.g. background, a subsequence can concisely capture the unstructured property of an event. To efficiently locate anomalous subsequences in a video space, we propose the maximum subsequence search. The proposed search algorithm integrates local anomaly scores into a global consistent detection so that the start and end of an abnormal event can be determined under false and missing detections. Experimental results on two public datasets show that our method is robust to the illumination change and achieve at least 80% localization rate which approximately doubles the accuracy of recent works. This study concludes that anomaly localization is crucial in finding abnormal events.