Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications

Blanco Adán, Carlos Roberto del and Mantecón del Valle, Tomás and Camplani, Massimo and Jaureguizar Núñez, Fernando and Salgado Álvarez de Sotomayor, Luis and García Santos, Narciso (2014). Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications. "Sensors", v. 14 (n. 2); pp. 1961-1987. ISSN 1424-8220. https://doi.org/10.3390/s140201961.

Description

Title: Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications
Author/s:
  • Blanco Adán, Carlos Roberto del
  • Mantecón del Valle, Tomás
  • Camplani, Massimo
  • Jaureguizar Núñez, Fernando
  • Salgado Álvarez de Sotomayor, Luis
  • García Santos, Narciso
Item Type: Article
Título de Revista/Publicación: Sensors
Date: February 2014
ISSN: 1424-8220
Volume: 14
Subjects:
Freetext Keywords: Depth sensors, foreground segmentation, video surveillance, Bayesian network
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2010-20412UnspecifiedUnspecifiedUnspecified

More information

Item ID: 37380
DC Identifier: http://oa.upm.es/37380/
OAI Identifier: oai:oa.upm.es:37380
DOI: 10.3390/s140201961
Official URL: http://www.mdpi.com/1424-8220/14/2/1961
Deposited by: Memoria Investigacion
Deposited on: 02 Sep 2015 17:57
Last Modified: 02 Sep 2015 17:57
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