Spatiotemporal vehicle tracking: the use of unsupervised learning-based segmentation and object tracking
Shu-Ching Chen; Mei-Ling Shyu; Srinivas Peeta; Chengcui Zhang
Robotics & Automation Magazine, IEEE
Volume 12, Issue 1, March 2005 Page(s): 50 - 58
Digital Object Identifier   10.1109/MRA.2005.1411419
Summary: 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.

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