Visual tracking via adaptive structural local sparse appearance model

Xu Jia, Huchuan Lu, Ming Hsuan Yang

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

1052 Citations (Scopus)

Abstract

Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based trackers only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the target and the background, and hence may fail with more possibility when there is similar object or occlusion in the scene. In this paper we develop a simple yet robust tracking method based on the structural local sparse appearance model. This representation exploits both partial information and spatial information of the target based on a novel alignment-pooling method. The similarity obtained by pooling across the local patches helps not only locate the target more accurately but also handle occlusion. In addition, we employ a template update strategy which combines incremental subspace learning and sparse representation. This strategy adapts the template to the appearance change of the target with less possibility of drifting and reduces the influence of the occluded target template as well. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Original languageEnglish
Title of host publication2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Pages1822-1829
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

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Jia, X., Lu, H., & Yang, M. H. (2012). Visual tracking via adaptive structural local sparse appearance model. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 1822-1829). [6247880] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2012.6247880
Jia, Xu ; Lu, Huchuan ; Yang, Ming Hsuan. / Visual tracking via adaptive structural local sparse appearance model. 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012. 2012. pp. 1822-1829 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).
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Jia, X, Lu, H & Yang, MH 2012, Visual tracking via adaptive structural local sparse appearance model. in 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012., 6247880, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1822-1829, 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012, Providence, RI, United States, 12/6/16. https://doi.org/10.1109/CVPR.2012.6247880

Visual tracking via adaptive structural local sparse appearance model. / Jia, Xu; Lu, Huchuan; Yang, Ming Hsuan.

2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012. 2012. p. 1822-1829 6247880 (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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Jia X, Lu H, Yang MH. Visual tracking via adaptive structural local sparse appearance model. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012. 2012. p. 1822-1829. 6247880. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2012.6247880