Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66217
Title: Compressive sensing based visual tracking using multi-task sparse learning method
Authors: Kang, B
Zhang, LH
Zhu, WP
Lun, DPK 
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016, 2016, 7752465 How to cite?
Abstract: In this paper, we propose a compressive sensing based framework for robust visual tracking. As a key part of the tracking framework, a new multi-task sparse learning method is designed to estimate the observation likelihood in order to determine the best target. Compared with the traditional multi-task sparse learning method, our method uses compressed appearance features to achieve multi-task sparse representation. Experimental results show that the proposed visual tracking framework can achieve a better tracking performance than state-of-the-art tracking methods with a significantly reduced computational complexity.
Description: 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016, Yangzhou, China, 13-15 October 2016
URI: http://hdl.handle.net/10397/66217
ISBN: 9781509028603
DOI: 10.1109/WCSP.2016.7752465
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