  Sparse representation has been widely studied in visual tracking, which has
shown promising tracking performance. Despite a lot of progress, the visual
tracking problem is still a challenging task due to appearance variations over
time. In this paper, we propose a novel sparse tracking algorithm that well
addresses temporal appearance changes, by enforcing template representability
and temporal consistency (TRAC). By modeling temporal consistency, our
algorithm addresses the issue of drifting away from a tracking target. By
exploring the templates' long-term-short-term representability, the proposed
method adaptively updates the dictionary using the most descriptive templates,
which significantly improves the robustness to target appearance changes. We
compare our TRAC algorithm against the state-of-the-art approaches on 12
challenging benchmark image sequences. Both qualitative and quantitative
results demonstrate that our algorithm significantly outperforms previous
state-of-the-art trackers.
