27 April 2009 A change detection approach to moving object detection in low fame-rate video
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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.
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Reid Porter, Neal Harvey, James Theiler, "A change detection approach to moving object detection in low fame-rate video", Proc. SPIE 7341, Visual Information Processing XVIII, 73410S (27 April 2009); doi: 10.1117/12.818622; https://doi.org/10.1117/12.818622
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