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Proceedings Paper

Multi-frame filtering techniques for the detection and recognition of moving objects
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Paper Abstract

Traditional correlation filtering methods produce classification results by processing one image frame at a time. The filters are designed to yield well defined correlation peaks when a pattern or object of interest is present in the input image. However, the decision process is memory-less, and does not take advantage of the history of results on previous frames in a sequence. Recently, Kerekes and Kumar introduced a new Bayesian approach for multi-frame correlation that first produces an estimate of the object's location based on previous results, and then builds up the hypothesis using both the current data as well as the historical estimate. In this paper, we examine a simple approximation to their approach which directly uses the correlation filter outputs while avoiding the need for density functions and explicit probability calculations. Preliminary analysis shows that the simplified approach has the potential for also yielding significant performance improvements over the conventional approach based on individual frames.

Paper Details

Date Published: 15 October 2007
PDF: 8 pages
Proc. SPIE 6736, Unmanned/Unattended Sensors and Sensor Networks IV, 67360L (15 October 2007); doi: 10.1117/12.758164
Show Author Affiliations
Abhijit Mahalanobis, Lockheed Martin Missiles and Fire Control (United States)
B. V. K. Vijaya Kumar, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 6736:
Unmanned/Unattended Sensors and Sensor Networks IV
Edward M. Carapezza, Editor(s)

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