Title :
Event Detection Using Trajectory Clustering and 4-D Histograms
Author :
Jung, Cláudio Rosito ; Hennemann, Luciano ; Musse, Soraia Raupp
Author_Institution :
Univ. do Vale do Rio dos Sinos, Sao Leopoldo
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.
Keywords :
computer vision; image motion analysis; image sensors; object detection; 4D histograms; captured trajectories; event detection; global motion flows; trajectory clustering; Classification; event detection; histograms; mixtures of Gaussians (MoGs); surveillance; unusual motion;
Journal_Title :
Circuits and Systems for Video Technology, IEEE Transactions on
DOI :
10.1109/TCSVT.2008.2005600