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 language | English (US) |
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Title of host publication | Proceedings of the IEEE International Conference on Computer Vision |
Pages | 26-33 |
Number of pages | 8 |
Volume | 2 |
State | Published - Jan 1 2001 |
Event | 8th International Conference on Computer Vision - Vancouver, BC, United States Duration: Jul 9 2001 → Jul 12 2001 |
Other
Other | 8th International Conference on Computer Vision |
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Country/Territory | United States |
City | Vancouver, BC |
Period | 7/9/01 → 7/12/01 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition