Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 18, Issue 11, Pages 1565-1575Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2008.2005600
Keywords
Classification; event detection; histograms; mixtures of Gaussians (MoGs); surveillance; unusual motion
Categories
Funding
- CNPq
- HP Brazil RD
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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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