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

Blanco Adán, Carlos Roberto del; Mantecón del Valle, Tomás; Camplani, Massimo; Jaureguizar Núñez, Fernando; Salgado Álvarez de Sotomayor, Luis y 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.

Descripción

Título: Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications
Autor/es:
  • Blanco Adán, Carlos Roberto del
  • Mantecón del Valle, Tomás
  • Camplani, Massimo
  • Jaureguizar Núñez, Fernando
  • Salgado Álvarez de Sotomayor, Luis
  • García Santos, Narciso
Tipo de Documento: Artículo
Título de Revista/Publicación: Sensors
Fecha: Febrero 2014
Volumen: 14
Materias:
Palabras Clave Informales: Depth sensors, foreground segmentation, video surveillance, Bayesian network
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaTEC2010-20412Sin especificarSin especificarSin especificar

Más información

ID de Registro: 37380
Identificador DC: http://oa.upm.es/37380/
Identificador OAI: oai:oa.upm.es:37380
Identificador DOI: 10.3390/s140201961
URL Oficial: http://www.mdpi.com/1424-8220/14/2/1961
Depositado por: Memoria Investigacion
Depositado el: 02 Sep 2015 17:57
Ultima Modificación: 02 Sep 2015 17:57
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