Event Detection Using Trajectory Clustering and 4-D Histograms

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Abstract
In this paper, we propose a framework for event detection based on trajectory clustering and 4-D histograms. In the training period, captured trajectories are grouped into coherent clusters according to global motion flows. Within each cluster, the position and instantaneous velocity of each tracked object are used to build a 4-D motion histogram for the cluster. In the test period, each new trajectory is compared against the 4-D histograms of all clusters, so that its coherence with previously tracked objects can be evaluated. Experimental results showed that these criteria can be effectively used to measure the coherence of test trajectories with those in the training stage, allowing a range of events to be detected in surveillance and traffic applications.

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