Comparing discrimination and CFA for selecting tracking features

Damian M. Lyons; D. Frank Hsu

doi:10.1117/12.777787

17 March 2008 Comparing discrimination and CFA for selecting tracking features

Damian M. Lyons, D. Frank Hsu

Proceedings Volume 6974, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008; 697408 (2008) https://doi.org/10.1117/12.777787
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available