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Proceedings Paper

Feature selection for real-time tracking
Author(s): D. Frank Hsu; Damian M. Lyons; Jizhou Ai
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Paper Abstract

We address the problem of selecting features to improve automated video tracking of targets that undergo multiple mutual occlusions. As targets are occluded, different feature subsets and combinations of those features are effective in identifying the target and improving tracking performance. We use Combinatorial Fusion Analysis to develop a metric to dynamically select which subset of features will produce the most accurate tracking. In particular we show that the combination of a pair of features A and B will improve the accuracy only if (a) A and B have relative high performance, and (b) A and B are diverse. We present experimental results to illustrate the performance of the proposed metric.

Paper Details

Date Published: 18 April 2006
PDF: 8 pages
Proc. SPIE 6242, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006, 62420I (18 April 2006); doi: 10.1117/12.669177
Show Author Affiliations
D. Frank Hsu, Fordham Univ. (United States)
Damian M. Lyons, Fordham Univ. (United States)
Jizhou Ai, Fordham Univ. (United States)


Published in SPIE Proceedings Vol. 6242:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006
Belur V. Dasarathy, Editor(s)

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