  In this paper, we propose a robust visual tracking method which exploits the
relationships of targets in adjacent frames using patchwise joint sparse
representation. Two sets of overlapping patches with different sizes are
extracted from target candidates to construct two dictionaries with
consideration of joint sparse representation. By applying this representation
into structural sparse appearance model, we can take two-fold advantages.
First, the correlation of target patches over time is considered. Second, using
this local appearance model with different patch sizes takes into account local
features of target thoroughly. Furthermore, the position of candidate patches
and their occlusion levels are utilized simultaneously to obtain the final
likelihood of target candidates. Evaluations on recent challenging benchmark
show that our tracking method outperforms the state-of-the-art trackers.
