Robust scale-adaptive mean-shift for tracking

The mean-shift procedure is a popular object tracking algorithm since it is fast, easy to implement and performs well in a range of conditions. We address the problem of scale adaptation and present a novel theoretically justified scale estimation mechanism which relies solely on the mean-shift procedure for the Hellinger distance. We also propose two improvements of the mean-shift tracker that make the scale estimation more robust in the presence of background clutter. The first one is a novel histogram color weighting that exploits the object neighborhood to help discriminate the target called background ratio weighting (BRW). We show that the BRW improves performance of MS-like tracking methods in general. The second improvement boost the performance of the tracker with the proposed scale estimation by the introduction of a forward–backward consistency check and by adopting regularization terms that counter two major problems: scale expansion caused by background clutter and scale implosion on self-similar objects. The proposed mean-shift tracker with scale selection and BRW is compared with recent state-of-the-art algorithms on a dataset of 77 public sequences. It outperforms the reference algorithms in average recall, processing speed and it achieves the best score for 30% of the sequences – the highest percentage among the reference algorithms.