Anomaly detection and localization in crowded scenes using connected component analysis

This paper presents a new method for detecting abnormal events in the surveillance systems. This method does not employ object detection or tracking and thus it does not fail in the crowded scenes. In the first step of proposed method, the appropriate cell size is determined by calculating the prevalent size of the connected components. Then, the redundant information is eliminated and the important regions are extracted from training data. This preprocessing significantly reduces the volume of training data in the learning phase. Next, using the HOG descriptors and a multivariate Gaussian model, the appearance anomalies i.e. the abnormality in terms of physical characteristics is detected. Besides that, a simple algorithm is provided to detect the abnormal motion using the average optical flow of the cells. Experimental results on the UCSD-PED2 datasets show that the proposed method can reliably detect abnormal events in video sequences, outperforming the current state-of-the-art methods.