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Proceedings Paper

Online maintaining appearance model using particle filter
Author(s): Siying Chen; Tian Lan; Jianyu Wang; Guoqiang Ni
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Paper Abstract

Tracking by foreground matching heavily depends on the appearance model to establish object correspondences among frames and essentially, the appearance model should encode both the difference part between the object and background to guarantee the robustness and the stable part to ensure tracking consistency. This paper provides a solution for online maintaining appearance models by adjusting features in the model. Object appearance is co-modeled by a subset of Haar features selected from the over-complete feature dictionary which encodes the discriminative part of object appearance and the color histogram which describes the stable appearance. During the particle filtering process, feature values both from background patches and object observations are sampled efficiently by the aid of "foreground" and "background" particles respectively. Based on these sampled values, top-ranked discriminative features are added and invalid features are removed out to ensure the object being distinguishable from current background according to the evolving appearance model. The tracker based on this online appearance model maintaining technique has been tested on people and car tracking tasks and promising experimental results are obtained.

Paper Details

Date Published: 29 November 2007
PDF: 10 pages
Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 68331Y (29 November 2007); doi: 10.1117/12.755272
Show Author Affiliations
Siying Chen, Beijing Institute of Technology (China)
Tian Lan, Beijing Institute of Technology (China)
Jianyu Wang, Institute of Computing Technology (China)
Guoqiang Ni, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 6833:
Electronic Imaging and Multimedia Technology V
Liwei Zhou; Chung-Sheng Li; Minerva M. Yeung, Editor(s)

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