Interest points based object tracking via sparse representation

In this paper, we propose an interest point based object tracker in sparse representation (SR) framework. In the past couple of years, there have been many proposals for object tracking in sparse framework exhibiting robust performance in various challenging scenarios. One of the major issues with these SR trackers is its slow execution speed mainly attributed to the particle filter framework. In this paper, we propose a robust interest point based tracker in l₁ minimization framework that runs at real-time with better performance compared to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points are obtained via solving the proposed l₁ minimization problem. A robust matching criterion is proposed to prune the noisy matches. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance of the proposed tracker is bench marked with several complex video sequences and found to be fast and robust compared to reported state of the art trackers.