Title :
A dynamic pruning and feature selection strategy for real-time tracking
Author :
Hsu, D. Frank ; Lyons, Damian M.
Author_Institution :
Dept. of Comput. & Inf. Sci., Fordham Univ., Bronx, NY, USA
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.
Keywords :
feature extraction; real-time systems; sensor fusion; target tracking; video signal processing; dynamic pruning; feature selection; multisensor networks; rank-score graph; real-time tracking; sensor fusion; sorting; target tracking; video analysis; video classification error; Computer vision; Fuses; Information science; Laboratories; Robot vision systems; Robotics and automation; Surveillance; Target tracking; Time measurement; Virtual reality; Multisensor Networks; Sensor Fusion; Sorting/Searching; Target Tracking; Video Analysis;
Conference_Titel :
Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on
Print_ISBN :
0-7695-2249-1
DOI :
10.1109/AINA.2005.22