Drift Detection and Correction Post-Tracking

Accurate object tracking is a challenging problem due to numerous factors, that may cause the tracker to drift away from the target object. Typically, the output of a tracker is a bounding box (BB); such BB may not well discriminate the object from its background and may not be centered correctly around the object. This paper proposes a method that first detects, at each frame, if a tracker tends to drift by analyzing saliency features of the output BB of a tracker, and then applies automatic seeded object segmentation on the BB to correct the drift once detected. Such segmentation is meant to relocate (recenter) the BB adaptive to the object segmented. As seeds, we propose to use SIFT and salient points conditioned they are non-background pixels. Different than related work, our approach thus models drift external to a base tracker by examining its output BB at each and corrects drift, as needed, by updating that BB adaptive to segmentation. We show the ability of the proposed method to significantly improve the tracking quality of base trackers. We also show that the proposed method outperforms by far segmentation-based trackers.