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Change detection by probabilistic segmentation from monocular view

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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.

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Acknowledgments

This work was supported in part by the CONACyT, Mexico [DSc. Scholarship to F.H. and research grant 131369 to M.R.].

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  1. Centro de Investigacion en Matematicas A.C., Guanajuato Gto, 36240, Mexico

    Francisco J. Hernandez-Lopez & Mariano Rivera

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  1. Francisco J. Hernandez-Lopez
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Correspondence to Francisco J. Hernandez-Lopez.

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Hernandez-Lopez, F.J., Rivera, M. Change detection by probabilistic segmentation from monocular view. Machine Vision and Applications 25, 1175–1195 (2014). https://doi.org/10.1007/s00138-013-0564-3

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