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17 March 2008 Comparing discrimination and CFA for selecting tracking features
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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.
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Damian M. Lyons and D. Frank Hsu "Comparing discrimination and CFA for selecting tracking features", Proc. SPIE 6974, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008, 697408 (17 March 2008); https://doi.org/10.1117/12.777787
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