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ICCCN
2007
IEEE

Online Selection of Tracking Features using AdaBoost

13 years 11 months ago
Online Selection of Tracking Features using AdaBoost
In this paper, a novel feature selection algorithm for object tracking is proposed. This algorithm performs more robust than the previous works by taking the correlation between features into consideration. Pixels of object/background regions are first treated as training samples. The feature selection problem is then modeled as finding a good subset of features and constructing a compound likelihood image with better discriminability for the tracking process. By adopting the AdaBoost algorithm, we iteratively select one best feature which compensate the previous selected features and linearly combine the set of corresponding likelihood images to obtain the compound likelihood image. We include the proposed algorithm into the mean shift based tracking system. Experimental results demonstrate that the proposed algorithm achieve very promising results.
Ying-Jia Yeh, Chiou-Ting Hsu
Added 07 Dec 2010
Updated 07 Dec 2010
Type Conference
Year 2007
Where ICCCN
Authors Ying-Jia Yeh, Chiou-Ting Hsu
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