Frequent Trajectory Patterns Mining for Intelligent Visual Surveillance System

A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system. The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated. In addition, a fuzzy c-mcans (FCM) based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories. The algorithm can be further used to describe activities and identify anomalies. The experiments on two real scenes show that the algorithm is effective.