Feature enhancement for mean-shift based object tracking

In object tracking identifying the best feature which discriminates object and background improves the performance. Most of the existing methods do not consider the suitability of such features for the tracker. Here we enhance the discriminative features which elevate the tracker performance. To accommodate object and background variations over time we dynamically update the best feature using a distance measure. We demonstrate the performance of the resulting systems on the UNIVERSITÄT KARLSRUHE Image Sequences.