Online selection of the best k-feature subset for object tracking

In this paper, we propose a new feature subset evaluation method for feature selection in object tracking. According to the fact that a feature which is useless by itself could become a good one when it is used together with some other features, we propose to evaluate feature subsets as a whole for object tracking instead of scoring each feature individually and find out the most distinguishable subset for tracking. In the paper, we use a special tree to formalize the feature subset space. Then conditional entropy is used to evaluating feature subset and a simple but efficient greedy search algorithm is developed to search this tree to obtain the optimal k-feature subset quickly. Furthermore, our online k-feature subset selection method is integrated into particle filter for robust tracking. Extensive experiments demonstrate that k-feature subset selected by our method is more discriminative and thus can improve tracking performance considerably.

► Select the most effective feature subset in discriminating the tracking target and background regardless of how distinguishable a single feature is. ► Use a subset tree whose leaves represent all of the candidate subset. ► Develop greedy search algorithm to obtain the optimal k-feature subset for object tracking. ► Propose an online k-feature subset selection method for updating feature subset during tracking.