By Topic

Spatiotemporal vehicle tracking: the use of unsupervised learning-based segmentation and object tracking

Sign In

Full text access may be available.

To access full text, please use your member or institutional sign in.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Shu-Ching Chen ; Sch. of Comput. Sci., Florida Int. Univ., Miami, FL, USA ; Mei-Ling Shyu ; Peeta, S. ; Chengcui Zhang

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.

Published in:

Robotics & Automation Magazine, IEEE  (Volume:12 ,  Issue: 1 )