Adaptive appearance learning for visual object tracking
Paper i proceeding, 2011

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.

anisotropic mean shift

hybrid trackers

Visual object tracking

RANSAC

particle filters

SIFT

dynamic appearance

Författare

Zulfiqar Hasan Khan

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

1413-1416 5946678

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Signalbehandling

Datorseende och robotik (autonoma system)

DOI

10.1109/ICASSP.2011.5946678

ISBN

978-145770539-7