A Selective Tracking and Detection Framework with Target Enhanced Feature

Abstract

In the long time tracking, object representation and occlusion handling are two important challenges. We propose a novel selective tracking and detection framework in which a new probabilistic object-enhanced feature is integrated. Firstly, besides precise object appearance feature, we believe the neighboring foreground-background contrast is another key factor in the tracking. Hence we propose a foreground probability map to enhance the target and weaken the surrounding background. It is computed based on the object color distribution and its comparison with the surrounding background. Secondly, we introduce the selective tracking and detection framework that has two sets of conditions to control the detector activation and final result selection. The detector will only be activated when the tracker is not trustable, which is determined by the tracking confidence and foreground parochiality value. Then, given the tracking and detection results, the final output is selected in terms of their individual correspondence values. We have evaluated our methods on two popular benchmark datasets. Extensive experiments demonstrate that our algorithm performs favorably comparing with state-of-the-art methods.