Visual Tracking via Subspace Learning: A Discriminative Approach-Reference-Cited by-同舟云学术

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Visual Tracking via Subspace Learning: A Discriminative Approach

Published:2017-11-10 Issue:5 Volume:126 Page:515-536
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Sui Yao,Tang Yafei,Zhang Li,Wang Guanghui

Funder

National Natural Science Foundation of China

Joint Fund of Civil Aviation Research by the National Natural Science Foundation of China and Civil Aviation Administration

National Aeronautics and Space Administration

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/article/10.1007/s11263-017-1049-z/fulltext.html

Reference75 articles.

1. Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing (TSP), 50(2), 174–188.

2. Avidan, S. (2004). Support vector tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 26(8), 1064–1072.

3. Avidan, S. (2007). Ensemble tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 29(2), 261–271.

4. Babenko, B., Member, S., Yang, M. H., & Member, S. (2011). Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(8), 1619–1632.

5. Beck, A., & Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1), 183–202.

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