L0-Regularized Object Representation for Visual Tracking
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.29
Abstract
In this paper, we propose a robust visual tracking method by L0-regularized prior in a particle filter framework. In contrast to existing methods, the proposed method employs L0 norm to regularize the linear coefficients of incrementally updated linear basis. The sparsity constraint enables the tracker to effectively handle difficult challenges, such as occlusion or image corruption. To achieve realtime processing, we propose a fast and efficient numerical algorithm for solving the proposed L0-regularized model. Although it is an NP-hard problem, the proposed accelerated proximal gradient (APG) approach is guaranteed to converge to a solution quickly. Extensive experimental results on challenging video sequences demonstrate that the proposed method achieves state-of-the-art results both in accuracy and speed.
Session
Tracking
Files
Extended Abstract (PDF, 1 page, 246K)Paper (PDF, 12 pages, 2.1M)
Bibtex File
Presentation
Citation
Jinshan Pan, Jongwoo Lim, Zhixun Su and Ming-Hsuan Yang. L0-Regularized Object Representation for Visual Tracking. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.29
title = {L0-Regularized Object Representation for Visual Tracking},
author = {Pan, Jinshan and Lim, Jongwoo and Su, Zhixum and Yang, Ming-Hsuan},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.29 }
}