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Proceedings Paper

Adaptive particle filtering
Author(s): Mark R. Stevens; Dan Gutchess; Neal Checka; Magnús Snorrason
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Paper Abstract

Image exploitation algorithms for Intelligence, Surveillance and Reconnaissance (ISR) and weapon systems are extremely sensitive to differences between the operating conditions (OCs) under which they are trained and the extended operating conditions (EOCs) in which the fielded algorithms are tested. As an example, terrain type is an important OC for the problem of tracking hostile vehicles from an airborne camera. A system designed to track cars driving on highways and on major city streets would probably not do well in the EOC of parking lots because of the very different dynamics. In this paper, we present a system we call ALPS for Adaptive Learning in Particle Systems. ALPS takes as input a sequence of video images and produces labeled tracks. The system detects moving targets and tracks those targets across multiple frames using a multiple hypothesis tracker (MHT) tightly coupled with a particle filter. This tracker exploits the strengths of traditional MHT based tracking algorithms by directly incorporating tree-based hypothesis considerations into the particle filter update and resampling steps. We demonstrate results in a parking lot domain tracking objects through occlusions and object interactions.

Paper Details

Date Published: 9 May 2006
PDF: 12 pages
Proc. SPIE 6229, Intelligent Computing: Theory and Applications IV, 62290P (9 May 2006); doi: 10.1117/12.665955
Show Author Affiliations
Mark R. Stevens, Charles River Analytics, Inc. (United States)
Dan Gutchess, Charles River Analytics, Inc. (United States)
Neal Checka, Charles River Analytics, Inc. (United States)
Magnús Snorrason, Charles River Analytics, Inc. (United States)

Published in SPIE Proceedings Vol. 6229:
Intelligent Computing: Theory and Applications IV
Kevin L. Priddy; Emre Ertin, Editor(s)

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