Robust Scale-Adaptive Mean-Shift for Tracking

Mean-Shift tracking is a popular algorithm for object tracking since it is easy to implement and it is fast and robust. In this paper, we address the problem of scale adaptation of the Hellinger distance based Mean-Shift tracker. We start from a theoretical derivation of scale estimation in the Mean-Shift framework. To make the scale estimation robust and suitable for tracking, we introduce regularization terms that counter two major problem: (i) scale expansion caused by background clutter and (ii) scale implosion on self-similar objects. To further robustify the scale estimate, it is validated by a forward-backward consistency check. The proposed Mean-shift tracker with scale selection is compared with recent state-of-the-art algorithms on a dataset of 48 public color sequences and it achieved excellent results.