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Proceedings Article

Markov random fields for static foreground classification in surveillance systems

[+] Author Affiliations
Jack K. Fitzsimons

Trinity College Dublin (Ireland)

Thomas T. Lu

Jet Propulsion Lab. (United States)

Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 92171O (September 23, 2014); doi:10.1117/12.2062508
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From Conference Volume 9217

  • Applications of Digital Image Processing XXXVII
  • Andrew G. Tescher
  • San Diego, California, United States | August 17, 2014

abstract

We present a novel technique for classifying static foreground in automated airport surveillance systems between abandoned and removed objects by representing the image as a Markov Random Field. The proposed algorithm computes and compares the net probability of the region of interest before and after the event occurs, hence finding which fits more naturally with their respective backgrounds. Having tested on a dataset from the PETS 2006, PETS 2007, AVSS20074, CVSG, VISOR, CANDELA and WCAM datasets, the algorithm has shown capable of matching the results of the state-of-the-art, is highly parallel and has a degree of robustness to noise and illumination changes. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Jack K. Fitzsimons and Thomas T. Lu
" Markov random fields for static foreground classification in surveillance systems ", Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 92171O (September 23, 2014); doi:10.1117/12.2062508; http://dx.doi.org/10.1117/12.2062508


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