Online Weighted Clustering for Real-time Abnormal Event Detection in Video Surveillance

Detecting abnormal events in video surveillance is a challenging problem due to the large scale, stream fashion video data as well as the real-time constraint. In this paper, we present an online, adaptive, and real-time framework to address this problem. The spatial locations in a frame is partitioned into grids, in each grid the proposed Adaptive Multi-scale Histogram Optical Flow (AMHOF) features are extracted and modelled by an Online Weighted Clustering (OWC) algorithm. The AMHOFs which cannot be fit to a cluster with large weight are regarded as abnormal events. The OWC algorithm is simple to implement and computational efficient. In addition, we improve the detection performance by a Multiple Target Tracking (MTT) algorithm. Experimental results demonstrate our approach outperforms the state-of-the-art approaches in pixel-level rate of detection at a processing speed of 30 FPS.