  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.
