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Proceedings Paper

A robust visual tracking method with restricted Boltzmann machine based classifier
Author(s): Hanchi Lin; Guibo Luo; Yuesheng Zhu
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Paper Abstract

In general, visual trackers employ hand-crafted feature descriptors to track the object, which limits their performance. In this paper, a novel Restricted Boltzmann Machine based Tracker (RBMT) is proposed to enhance the robustness. RBMs are introduced to learn multiple feature descriptors for the different image cues which are transformed from the given images. A data augment method is introduced to online train the RBMs so as to make the learnt feature descriptors specific for different tracked objects. To make the proposed tracker adapted to drastic varying scenes, a feature selection method is also developed to fuse the multiple cues in feature level for the design of appearance-based classifiers. Our experimental results have shown that the proposed tracker can obtain promising performances compared with the other state-of-the-art approaches.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100335Q (29 August 2016); doi: 10.1117/12.2244145
Show Author Affiliations
Hanchi Lin, Peking Univ. (China)
Guibo Luo, Peking Univ. (China)
Yuesheng Zhu, Peking Univ. (China)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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