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Adaptive appearance learning for visual object tracking

Zulfiqar H. Khan (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling)
IEEE int'l conf. on Acoustics, Speech and Signal Processing (ICASSP 2011) Prague; 22 May 2011 through 27 May 2011 (15206149). p. 1413-1416 . (2011)
[Konferensbidrag, refereegranskat]

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 without 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 similarity and local point features correspondences-based tracker to improve [7]; (d) integration of adaptive appearance in joint appearance similarity and particle filter tracker under the Bayesian framework to improve [10]. Experimental results on a range of videos captured by a dynamic/stationary camera 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.

Nyckelord: Visual object tracking, dynamic appearance, SIFT, RANSAC, anisotropic mean shift, particle filters, hybrid trackers

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Denna post skapades 2011-03-01. Senast ändrad 2013-06-26.
CPL Pubid: 137399


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Institutionen för signaler och system, Signalbehandling (1900-2017)


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