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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 5/2014

01.07.2014 | Special Issue Paper

Change detection by probabilistic segmentation from monocular view

verfasst von: Francisco J. Hernandez-Lopez, Mariano Rivera

Erschienen in: Machine Vision and Applications | Ausgabe 5/2014

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Abstract

We present a method for foreground/background video segmentation (change detection) in real-time that can be used, in applications such as background subtraction or analysis of surveillance cameras. Our approach implements a probabilistic segmentation based on the Quadratic Markov Measure Field models. This framework regularizes the likelihood of each pixel belonging to each one of the classes (background or foreground). We propose a new likelihood that takes into account two cases: the first one is when the background is static and the foreground might be static or moving (Static Background Subtraction), the second one is when the background is unstable and the foreground is moving (Unstable Background Subtraction). Moreover, our likelihood is robust to illumination changes, cast shadows and camouflage situations. We implement a parallel version of our algorithm in CUDA using a NVIDIA Graphics Processing Unit in order to fulfill real-time execution requirements.
Vorheriger Artikel Background subtraction: separating the modeling and the inference
Nächster Artikel Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction

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Metadaten
Titel
Change detection by probabilistic segmentation from monocular view
verfasst von
Francisco J. Hernandez-Lopez
Mariano Rivera
Publikationsdatum
01.07.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 5/2014
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-013-0564-3

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