A Bayesian approach to activity detection in video using multi-frame correlation filters

Abhijit Mahalanobis; Robert Stanfill; Kenny Chen

doi:10.1117/12.884771

19 May 2011 A Bayesian approach to activity detection in video using multi-frame correlation filters

Abhijit Mahalanobis, Robert Stanfill, Kenny Chen

Author Affiliations +

Proceedings Volume 8049, Automatic Target Recognition XXI; 80490P (2011) https://doi.org/10.1117/12.884771
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Abhijit Mahalanobis, Robert Stanfill, and Kenny Chen "A Bayesian approach to activity detection in video using multi-frame correlation filters", Proc. SPIE 8049, Automatic Target Recognition XXI, 80490P (19 May 2011); https://doi.org/10.1117/12.884771

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