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

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...

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Bibliographic Details
Published in19th International Conference on Advanced Information Networking and Applications (AINA'05) Volume 1 (AINA papers) Vol. 1; pp. 117 - 124 vol.1
Main Authors Hsu, D.F., Lyons, D.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Computer vision
Fuses
Information science
Laboratories
Multisensor Networks
Robot vision systems
Robotics and automation
Sensor Fusion
Sorting/Searching
Surveillance
Target tracking
Time measurement
Video Analysis
Virtual reality
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Summary: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.
ISBN:9780769522494
0769522491
ISSN:1550-445X
2332-5658
DOI:10.1109/AINA.2005.22