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Proceedings Paper

A Bayesian approach to activity detection in video using multi-frame correlation filters
Author(s): Abhijit Mahalanobis; Robert Stanfill; Kenny Chen
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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
Show Author Affiliations
Abhijit Mahalanobis, 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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