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Proceedings Paper

A change detection approach to moving object detection in low fame-rate video
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Paper Abstract

Moving object detection is of significant interest in temporal image analysis since it is a first step in many object identification and tracking applications. A key component in almost all moving object detection algorithms is a pixellevel classifier, where each pixel is predicted to be either part of a moving object or part of the background. In this paper we investigate a change detection approach to the pixel-level classification problem and evaluate its impact on moving object detection. The change detection approach that we investigate was previously applied to multi- and hyper-spectral datasets, where images were typically taken several days, or months apart. In this paper, we apply the approach to lowframe rate (1-2 frames per second) video datasets.

Paper Details

Date Published: 27 April 2009
PDF: 8 pages
Proc. SPIE 7341, Visual Information Processing XVIII, 73410S (27 April 2009); doi: 10.1117/12.818622
Show Author Affiliations
Reid Porter, Los Alamos National Lab. (United States)
Neal Harvey, Los Alamos National Lab. (United States)
James Theiler, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 7341:
Visual Information Processing XVIII
Zia-Ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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