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Proceedings Paper

Online visual tracking based on updating with smoothing
Author(s): Jin Zhang; Kai Liu; Fei Cheng; YunSong Li
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Paper Abstract

Visual tracking is an important task in computer vision. Despite many researches have been done in this area, some problems remain. One of the problems is drifting. To handle the problem, a new appearance model update method based on a forward filtering backward smoothing particle filter is proposed in this paper. A smoothing of previous appearance model is performed by exploiting information of current frame instead of updating instantly in traditional tracking methods. It has been shown that smoothing based on future observations makes previous and current predictions more accurate, thus the appearance model update by our approach is more accurate. And at the same time, online tracking is achieved compared with some previous work in which the smoothing is done in an offline way. With the smoothing procedure, the tracker is more accurate and less likely to drift than traditional ones. Experimental results demonstrate the effectiveness of the proposed method.

Paper Details

Date Published: 22 May 2014
PDF: 8 pages
Proc. SPIE 9124, Satellite Data Compression, Communications, and Processing X, 91241F (22 May 2014); doi: 10.1117/12.2053153
Show Author Affiliations
Jin Zhang, Xidian Univ. (China)
Kai Liu, Xidian Univ. (China)
Fei Cheng, Xidian Univ. (China)
YunSong Li, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 9124:
Satellite Data Compression, Communications, and Processing X
Bormin Huang; Chein-I Chang; José Fco. López, Editor(s)

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