Proceedings Paper
A Bayesian approach to activity detection in video using multi-frame correlation filters| Format | Member Price | Non-Member Price |
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Paper Abstract
Multi-frame correlation filters have been recently reported in the literature for the detection of
moving objects. Introduced by Kerekes and Kumar [5], this technique uses a motion model to
accumulate evidence over time in a Bayesian framework to improve the receiver operating
characteristic (ROC) curve. In this paper, we generalize the approach to not only detect objects,
but also their activities by using separate motion models to represent each activity. We also
discuss results of preliminary simulations using publicly released aerial data set to illustrate the
concept.
Paper Details
Date Published: 19 May 2011
PDF: 12 pages
Proc. SPIE 8049, Automatic Target Recognition XXI, 80490P (19 May 2011); doi: 10.1117/12.884771
Published in SPIE Proceedings Vol. 8049:
Automatic Target Recognition XXI
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)
PDF: 12 pages
Proc. SPIE 8049, Automatic Target Recognition XXI, 80490P (19 May 2011); doi: 10.1117/12.884771
Show Author Affiliations
Abhijit Mahalanobis, Lockheed Martin Missiles and Fire Control (United States)
Robert Stanfill, Lockheed Martin Missiles and Fire Control (United States)
Robert Stanfill, Lockheed Martin Missiles and Fire Control (United States)
Kenny Chen, Lockheed Martin Missiles and Fire Control (United States)
Published in SPIE Proceedings Vol. 8049:
Automatic Target Recognition XXI
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)
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