Part-Based Tracking by Sampling
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We propose a novel part-based method for tracking an arbitrary object in challenging video sequences, focusing on robustly tracking under the effects of camera motion and object motion change. Each of a group of tracked image patches on the target is represented by pairs of RGB pixel samples and counts of how many pixels in the patch are similar to them. This empirically characterises the underlying colour distribution of the patches and allows for matching using the Bhattacharyya distance. Candidate patch locations are generated by applying non-shearing affine transformations to the patches' previous locations, followed by local optimisation. Experiments using the VOT2016 dataset show that our tracker out-performs all other part-based trackers in terms of robustness to camera motion and object motion change.
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