Compressive sensing based visual tracking using multi-task sparse learning method

Bin Kang, Ling Hua Zhang, Wei Ping Zhu, Pak Kong Lun

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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.
Original languageEnglish
Title of host publication2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
PublisherIEEE
ISBN (Electronic)9781509028603
DOIs
Publication statusPublished - 21 Nov 2016
Event8th International Conference on Wireless Communications and Signal Processing, WCSP 2016 - Yangzhou, China
Duration: 13 Oct 201615 Oct 2016

Conference

Conference8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
Country/TerritoryChina
CityYangzhou
Period13/10/1615/10/16

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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