Abnormal Motion Pattern Detection in Surveillance Video Sequences by Clustering Approach

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Abstract

Surveillance cameras are widely being used in public places for security and monitoring purposes. Detecting abnormal motion pattern from surveillance video sequences is challenging task. Most of the existing methods are based on supervised technique. Supervised method groups feature points into normal and abnormal motion pattern using classifier. But anomalous event are contextual so this paper focuses on unsupervised learning method of finding abnormal motion pattern. Contextual abnormality can not be detected by supervised method in every surveillance video sequences. This proposed approach works without the need of training phase. By extracting trajectory features by dense optical flow, speed of moving objects are taken into consideration for unsupervised motion pattern in video sequences. K-means clustering approach is simple to implement and computationally efficient. By applying such clustering method, dominant motion group and anomalous group are well separated. Experimental results demonstrate this proposed approach outperforms the state-of-art approaches on standard dataset.