Document Type
Journal Article
Abstract
This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating the linear fractional transformation. This provides a better coverage of the feature space and yields a finer estimate of the posterior density. Experiments conducted on traffic video surveillance sequences show that the variance of the estimated trajectory is reduced, resulting in more robust tracking.
RIS ID
31286
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