Adaptive appearance learning for visual object tracking
Paper in proceedings, 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

Author

Zulfiqar Hasan Khan

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

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

15206149 (ISSN)

1413-1416 5946678

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/ICASSP.2011.5946678

ISBN

978-145770539-7

More information

Created

10/6/2017