Saliency-based abnormal event detection in crowded scenes

Abnormal event detection plays a critical role for intelligent video surveillance, and detection in crowded scenes is a challenging but more practical task. We present an abnormal event detection method for crowded video. Region-wise modeling is proposed to address the inconsistent detected motion of the same object due to different depths of field. Comparing to traditional block-wise modeling, the region-wise method not only can reduce heavily the number of models to be built but also can enrich the samples for training the normal events model. In order to reduce the computational burden and make the region-based anomaly detection feasible, a saliency detection technique is adopted in this paper. By identifying the salient parts of the image sequences, the irrelevant blocks are ignored, which removes the disturbance and improves the detection performance further. Experiments on the benchmark dataset and comparisons with the state-of-the-art algorithms validate the advantages of the proposed method. (C) 2016 SPIE and IS&T