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Sensors 2014, 14(2), 1961-1987;

Foreground Segmentation in Depth Imagery Using Depth and Spatial Dynamic Models for Video Surveillance Applications

Grupo de Tratamiento de Imágenes, E.T.S.I de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain
Video Processing and Understanding Laboratory, Universidad Autónoma de Madrid, Madrid 28049, Spain
Author to whom correspondence should be addressed.
Received: 16 December 2013 / Revised: 14 January 2014 / Accepted: 17 January 2014 / Published: 24 January 2014
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2013)
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Low-cost systems that can obtain a high-quality foreground segmentation almostindependently of the existing illumination conditions for indoor environments are verydesirable, especially for security and surveillance applications. In this paper, a novelforeground segmentation algorithm that uses only a Kinect depth sensor is proposedto satisfy the aforementioned system characteristics. This is achieved by combininga mixture of Gaussians-based background subtraction algorithm with a new Bayesiannetwork that robustly predicts the foreground/background regions between consecutivetime steps. The Bayesian network explicitly exploits the intrinsic characteristics ofthe depth data by means of two dynamic models that estimate the spatial and depthevolution of the foreground/background regions. The most remarkable contribution is thedepth-based dynamic model that predicts the changes in the foreground depth distributionbetween consecutive time steps. This is a key difference with regard to visible imagery,where the color/gray distribution of the foreground is typically assumed to be constant.Experiments carried out on two different depth-based databases demonstrate that theproposed combination of algorithms is able to obtain a more accurate segmentation of theforeground/background than other state-of-the art approaches. View Full-Text
Keywords: depth sensors; foreground segmentation; video surveillance; Bayesian network depth sensors; foreground segmentation; video surveillance; Bayesian network
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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del-Blanco, C.R.; Mantecón, T.; Camplani, M.; Jaureguizar, F.; Salgado, L.; García, N. Foreground Segmentation in Depth Imagery Using Depth and Spatial Dynamic Models for Video Surveillance Applications. Sensors 2014, 14, 1961-1987.

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