27 April 2009 A change detection approach to moving object detection in low fame-rate video
Author Affiliations +
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Reid Porter, Reid Porter, Neal Harvey, Neal Harvey, James Theiler, 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


Invariance concepts in spectral analysis
Proceedings of SPIE (May 04 2017)
Hyperspectral object tracking using small sample size
Proceedings of SPIE (May 12 2010)
Higher-order statistical steganalysis of palette images
Proceedings of SPIE (June 19 2003)
Resampling approach for anomalous change detection
Proceedings of SPIE (May 06 2007)
Progress in the theory of continuum fusion
Proceedings of SPIE (May 08 2012)

Back to Top