Adaptive appearance learning for visual object tracking

This paper addresses online learning of reference object distribution in the context of two hybrid tracking schemes that combine the mean shift with local point feature correspondences, and the mean shift under the Bayesian framework, respectively. The reference object distribution is built up by a kernel-weighted color histogram. The main contributions of the proposed schemes includes: (a) an adaptive learning strategy that seeks to update the reference object distribution when the changes are caused by the intrinsic object dynamic with out partial occlusion/intersection; (b) novel dynamic maintenance of object feature points by exploring both foreground and background sets; (c) integration of adaptive appearance and local point features in joint object appearance similar ity and local point features correspondences-based tracker to improve; (d) integration of adaptive appearance in joint appearance similarity and particle filter tracker under the Bayesian framework to improve. Experimental results on a range of videos captured by a dynamic/stationary cam era demonstrate the effectiveness of the proposed schemes in terms of robustness to partial occlusions, tracking drifts and tightness and accuracy of tracked bounding box. Comparisons are also made with the two hybrid trackers together with 3 existing trackers.