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http://dx.doi.org/10.3745/KIPSTB.2005.12B.3.273

Integration of Condensation and Mean-shift algorithms for real-time object tracking  

Cho Sang-Hyun (가톨릭대학교 컴퓨터정보공학과)
Kang Hang-Bong (가톨릭대학교 컴퓨터정보공학부)
Publication Information
The KIPS Transactions:PartB / v.12B, no.3, 2005 , pp. 273-282 More about this Journal
Abstract
Real-time Object tracking is an important field in developing vision applications such as surveillance systems and vision based navigation. mean-shift algerian and Condensation algorithm are widely used in robust object tracking systems. Since the mean-shift algorithm is easy to implement and is effective in object tracking computation, it is widely used, especially in real-time tracking systems. One of the drawbacks is that it always converges to a local maximum which may not be a global maximum. Therefore, in a cluttered environment, the Mean-shift algorithm does not perform well. On the other hand, since it uses multiple hypotheses, the Condensation algorithm is useful in tracking in a cluttered background. Since it requires a complex object model and many hypotheses, it contains a high computational complexity. Therefore, it is not easy to apply a Condensation algorithm in real-time systems. In this paper, by combining the merits of the Condensation algorithm and the mean-shift algorithm we propose a new model which is suitable for real-time tracking. Although it uses only a few hypotheses, the proposed method use a high-likelihood hypotheses using mean-shift algorithm. As a result, we can obtain a better result than either the result produced by the Condensation algorithm or the result produced by the mean-shift algorithm.
Keywords
Mean-shift; Condensation; Particle Filter; Real-time Tracking;
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