
Proceedings Paper
Foreground estimation in motion imagery using multi-frame change detection techniquesFormat | Member Price | Non-Member Price |
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Paper Abstract
Using multi-frame change detection methods, we estimate which pixels include objects that are in motion relative to
the background. We utilize both a sequential statistical change detection method and a sparsity-based change detection
method. We perform foreground estimation in videos in which the background is static as well as in images in which
apparent background motion is induced by camera motion. We show the results of our techniques on the background
subtraction data set from the Statistical Visual Computing Lab at the University of California, San Diego(UCSD).
Paper Details
Date Published: 16 May 2013
PDF: 6 pages
Proc. SPIE 8740, Motion Imagery Technologies, Best Practices, and Workflows for Intelligence, Surveillance, and Reconnaissance (ISR), and Situational Awareness, 87400G (16 May 2013); doi: 10.1117/12.2015931
Published in SPIE Proceedings Vol. 8740:
Motion Imagery Technologies, Best Practices, and Workflows for Intelligence, Surveillance, and Reconnaissance (ISR), and Situational Awareness
Donnie Self, Editor(s)
PDF: 6 pages
Proc. SPIE 8740, Motion Imagery Technologies, Best Practices, and Workflows for Intelligence, Surveillance, and Reconnaissance (ISR), and Situational Awareness, 87400G (16 May 2013); doi: 10.1117/12.2015931
Show Author Affiliations
Andrew J. Lingg, Wright State Univ. (United States)
Brian D. Rigling, Wright State Univ. (United States)
Published in SPIE Proceedings Vol. 8740:
Motion Imagery Technologies, Best Practices, and Workflows for Intelligence, Surveillance, and Reconnaissance (ISR), and Situational Awareness
Donnie Self, Editor(s)
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