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A dynamic pruning and feature selection strategy for real-time tracking

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2 Author(s)
Hsu, D.F. ; Dept. of Comput. & Inf. Sci., Fordham Univ., Bronx, NY, USA ; Lyons, D.M.

Automated video tracking is useful in a number of applications such as surveillance, multisensor networks, robotics and virtual reality. In this paper we investigate an approach to tracking based on fusing the output of a collection of video trackers, each attending to a different feature or cue on the target. We show both theoretically and experimentally that the method used to prune the growth of target hypotheses can have a great impact on the trackers performance, and indirectly, change the benefit of using linear score combination as opposed to a non-linear rank combination for fusion. We also show that the rank-score graph defined by Hsu and Taksa can be used to select a subset of features to fuse to reduce classification error.

Published in:

Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on  (Volume:1 )

Date of Conference:

28-30 March 2005