Optimizing Distribution-based Matching by Random Subsampling

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Optimizing Distribution-based Matching by Random Subsampling
We boost the efficiency and robustness of distributionbased matching by random subsampling which results in the minimum number of samples required to achieve a specified probability that a candidate sampling distribution is a good approximation to the model distribution. The improvement is demonstrated with applications to object detection, Mean-Shift tracking using color distributions and tracking with improved robustness for low-resolution video sequences. The problem of minimizing the number of samples required for robust distribution matching is formulated as a constrained optimization problem with the specified probability as the objective function. We show that surprisingly Mean-Shift tracking using our method requires very few samples. Our experiments demonstrate that robust tracking can be achieved with even as few as 5 random samples from the distribution of the target candidate. This leads to a considerably reduced computational complexity that is also independent of object ...
Alex Po Leung, Shaogang Gong
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Alex Po Leung, Shaogang Gong
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