Patchwise joint sparse tracking with occlusion detection
Abstract: Abstract—This paper presents a robust tracking approach to
handle challenges such as occlusion and appearance change.
Here, the target is partitioned into a number of patches. Then,
the appearance of each patch is modeled using a dictionary
composed of corresponding target patches in previous frames.
In each frame, the target is found among a set of candidates
generated by a particle filter, via a likelihood measure that is
shown to be proportional to the sum of patch-reconstruction
errors of each candidate. Since the target’s appearance often
changes slowly in a video sequence, it is assumed that the
target in the current frame and the best candidates of a small
number of previous frames, belong to a common subspace.
This is imposed using joint sparse representation to enforce the
target and previous best candidates to have a common sparsity
pattern. Moreover, an occlusion detection scheme is proposed
that uses patch-reconstruction errors and a prior probability of
occlusion, extracted from an adaptive Markov chain, to calculate
the probability of occlusion per patch. In each frame, occluded
patches are excluded when updating the dictionary. Extensive
experimental results on several challenging sequences shows that
the proposed method outperforms state-of-the-art trackers.
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