Robust object tracking via sparsity-based collaborative model

Wei Zhong, Huchuan Lu, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

849 Citations (Scopus)

Abstract

In this paper we propose a robust object tracking algorithm using a collaborative model. As the main challenge for object tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations. We develop a sparsity-based discriminative classifier (SD-C) and a sparsity-based generative model (SGM). In the S-DC module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In the SGM module, we propose a novel histogram-based method that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.

Original languageEnglish
Title of host publication2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Pages1838-1845
Number of pages8
DOIs
Publication statusPublished - 2012 Oct 1
Event2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 - Providence, RI, United States
Duration: 2012 Jun 162012 Jun 21

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
CountryUnited States
CityProvidence, RI
Period12/6/1612/6/21

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Zhong, W., Lu, H., & Yang, M. H. (2012). Robust object tracking via sparsity-based collaborative model. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 1838-1845). [6247882] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2012.6247882
Zhong, Wei ; Lu, Huchuan ; Yang, Ming Hsuan. / Robust object tracking via sparsity-based collaborative model. 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012. 2012. pp. 1838-1845 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).
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Zhong, W, Lu, H & Yang, MH 2012, Robust object tracking via sparsity-based collaborative model. in 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012., 6247882, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1838-1845, 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012, Providence, RI, United States, 12/6/16. https://doi.org/10.1109/CVPR.2012.6247882

Robust object tracking via sparsity-based collaborative model. / Zhong, Wei; Lu, Huchuan; Yang, Ming Hsuan.

2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012. 2012. p. 1838-1845 6247882 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Zhong W, Lu H, Yang MH. Robust object tracking via sparsity-based collaborative model. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012. 2012. p. 1838-1845. 6247882. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2012.6247882