A co-inference approach to robust visual tracking

Y. Wu*, T. S. Huang

*Corresponding author for this work

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

111 Scopus citations

Abstract

Visual tracking could be treated as a parameter estimation problem of target representation based on observations in image sequences. A richer target representation would incur better chances of successful tracking in cluttered and dynamic environments. However, the dimensionality of target's state space also increases making tracking a formidable estimation problem. In this paper, the problem of tracking and integrating multiple cues is formulated in a probabilistic framework and represented by a factorized graphical model. Structured variational analysis of such graphical model factorizes different modalities and suggests a co-inference process among these modalities. A sequential Monte Carlo algorithm is proposed to give an efficient approximation of the co-inference based on the importance sampling technique. This algorithm is implemented in real-time at around 30Hz. Specifically, tracking both position, shape and color distribution of a target is investigated in this paper. Our extensive experiments show that the proposed algorithm performs robustly in a large variety of tracking scenarios. The approach presented in this paper has the potential to solve other sensor fusion problems.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
Pages26-33
Number of pages8
Volume2
StatePublished - Jan 1 2001
Event8th International Conference on Computer Vision - Vancouver, BC, United States
Duration: Jul 9 2001Jul 12 2001

Other

Other8th International Conference on Computer Vision
CountryUnited States
CityVancouver, BC
Period7/9/017/12/01

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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    Wu, Y., & Huang, T. S. (2001). A co-inference approach to robust visual tracking. In Proceedings of the IEEE International Conference on Computer Vision (Vol. 2, pp. 26-33)