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http://dx.doi.org/10.5391/JKIIS.2004.14.5.539

Visual Object Tracking based on Particle Filters with Multiple Observation  

Koh, Hyeung-Seong (중앙대학교 공과대학 전자전기공학부)
Jo, Yong-Gun (중앙대학교 공과대학 전자전기공학부)
Kang, Hoon (중앙대학교 공과대학 전자전기공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.14, no.5, 2004 , pp. 539-544 More about this Journal
Abstract
We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.
Keywords
object tracking; CONDENSATION; Particle Filters; Active Contour;
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