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Proceedings Paper

Comparing discrimination and CFA for selecting tracking features
Author(s): Damian M. Lyons; D. Frank Hsu
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Paper Abstract

The ability of a tracker to isolate the foreground target from the background of an image is crucially dependent on the set of features selected for tracking. Collins & Liu [2] propose an on-line, adaptive approach to selecting the set of features based on the insight that the set of features that best discriminate between target and background classes is the best set to use for tracking. In previous work [10], we have proposed an approach based on Combinatorial Fusion Analysis for selecting features for Real-Time tracking. We discuss the relative merits of the two methods and motivate their combination to produce an improved tracking system. We show several results from a difficult tracking sequence with human targets to demonstrate the effectiveness of the combined system.

Paper Details

Date Published: 17 March 2008
PDF: 9 pages
Proc. SPIE 6974, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008, 697408 (17 March 2008); doi: 10.1117/12.777787
Show Author Affiliations
Damian M. Lyons, Fordham Univ. (United States)
D. Frank Hsu, Fordham Univ. (United States)

Published in SPIE Proceedings Vol. 6974:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008
Belur V. Dasarathy, Editor(s)

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