Using appearance re-matching to improve real-time compressive tracking
Pages 28 - 33
Abstract
A small number of randomly generated linear measurements can preserve most of the salient information of one compressible image according to compress sensing theory. Using these measurements as features can greatly improve the speed of detection based tracking methods, and deal with the problems caused by occlusion, illumination change, pose variation and motion blur to some extent. This paper addressed to improve the state-of-the-art real-time compressive object tracking algorithm, which extracted low-dimensional multistate features of object and background, then used naïve Bayesian classifier combined with online updating mechanism to track object in real-time under the compressed domain. On the basis of its tracking results, we rematch the first 30 candidate targets with online appearance model to search for the optimum tracking position. The experimental results in lot of challenging test sequences show that the proposed algorithm has promising potential.
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Index Terms
- Using appearance re-matching to improve real-time compressive tracking
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Information
Published In
August 2013
419 pages
ISBN:9781450322522
DOI:10.1145/2499788
- Conference Chair:
- Tat-Seng Chua,
- General Chairs:
- Ke Lu,
- Tao Mei,
- Xindong Wu
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Sponsors
- NSF of China: National Natural Science Foundation of China
- University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
- Beijing ACM SIGMM Chapter
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 August 2013
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- Research-article
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ICIMCS '13
Sponsor:
- NSF of China
- University of Sciences & Technology, Hefei
ICIMCS '13: International Conference on Internet Multimedia Computing and Service
August 17 - 19, 2013
Huangshan, China
Acceptance Rates
ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;
Overall Acceptance Rate 163 of 456 submissions, 36%
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