Multihypothesis trajectory analysis for robust visual tracking

Dae Youn Lee, Jae Young Sim, Chang-Su Kim

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

30 Citations (Scopus)

Abstract

The notion of multihypothesis trajectory analysis (MTA) for robust visual tracking is proposed in this work. We employ multiple component trackers using texture, color, and illumination invariant features, respectively. Each component tracker traces a target object forwardly and then backwardly over a time interval. By analyzing the pair of the forward and backward trajectories, we measure the robustness of the component tracker. To this end, we extract the geometry similarity, the cyclic weight, and the appearance similarity from the forward and backward trajectories. We select the optimal component tracker to yield the maximum robustness score, and use its forward trajectory as the final tracking result. Experimental results show that the proposed MTA tracker improves the robustness and the accuracy of tracking, outperforming the state-of-the-art trackers on a recent benchmark dataset.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages5088-5096
Number of pages9
Volume07-12-June-2015
ISBN (Print)9781467369640
DOIs
Publication statusPublished - 2015 Oct 14
EventIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States
Duration: 2015 Jun 72015 Jun 12

Other

OtherIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
CountryUnited States
CityBoston
Period15/6/715/6/12

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Lee, D. Y., Sim, J. Y., & Kim, C-S. (2015). Multihypothesis trajectory analysis for robust visual tracking. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 07-12-June-2015, pp. 5088-5096). [7299144] IEEE Computer Society. https://doi.org/10.1109/CVPR.2015.7299144