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