Document Type
Article
Keywords
Sensor Fusion, Target Tracking, Multisensor Networks, Video Analysis, Sorting/Searching
Disciplines
Computer Engineering | Robotics
Abstract
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.
Article Number
1033
Publication Date
3-2005
Recommended Citation
Hsu, D. Frank and Lyons, Damian M., "A Dynamic Pruning and Feature Selection Strategy for Real-Time Tracking" (2005). Faculty Publications. 33.
https://research.library.fordham.edu/frcv_facultypubs/33
Comments
IEEE International Conference on Advanced Information Networking and Applications, March 2005, Taipei Taiwan.
This research was conducted at the Fordham University Robotics and Computer Vision Lab. For more information about graduate programs in Computer Science, see http://www.cis.fordham.edu/graduate.html, and the Fordham University Graduate School of Arts and Sciences, see http://www.fordham.edu/gsas.