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Proceedings Paper

Markov random fields for static foreground classification in surveillance systems
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Paper 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.

Paper Details

Date Published: 23 September 2014
PDF: 10 pages
Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 92171O (23 September 2014); doi: 10.1117/12.2062508
Show Author Affiliations
Jack K. Fitzsimons, Trinity College Dublin (Ireland)
Thomas T. Lu, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 9217:
Applications of Digital Image Processing XXXVII
Andrew G. Tescher, Editor(s)

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