Diversity-based classifier selection for adaptive object tracking

In this work we propose a novel pairwise diversity measure, that recalls the Fisher linear discriminant, to construct a classifier ensemble for tracking a non-rigid object in a complex environment. A subset of constantly updated classifiers is selected exploiting their capability to distinguish the target from the background and, at the same time, promoting independent errors. This reduced ensemble is employed in the target search phase, speeding up the application of the system and maintaining the performance comparable to state of the art algorithms. Experiments have been conducted on a Pan-Tilt-Zoom camera video sequence to demonstrate the effectiveness of the proposed approach coping with pose variations of the target.