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In this paper, a framework for spatiotemporal vehicle tracking using unsupervised learning-based segmentation and object tracking is presented. An adaptive background learning and subtraction method is proposed and applied to two real-traffic video sequences to obtain more accurate spatiotemporal information on the vehicle objects. As demonstrated in the experiments, almost all vehicle objects are successfully identified through this framework.
Date of Publication: March 2005