Sensors 2014, 14(2), 1961-1987; doi:10.3390/s140201961
Article

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

1 Grupo de Tratamiento de Imágenes, E.T.S.I de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain 2 Video Processing and Understanding Laboratory, Universidad Autónoma de Madrid, Madrid 28049, Spain
* Author to whom correspondence should be addressed.
Received: 16 December 2013; in revised form: 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)
PDF Full-text Download PDF Full-Text [31619 KB, uploaded 24 January 2014 09:52 CET]
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.
Keywords: depth sensors; foreground segmentation; video surveillance; Bayesian network

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

MDPI and ACS Style

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.

AMA Style

del-Blanco CR, 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(2):1961-1987.

Chicago/Turabian Style

del-Blanco, Carlos R.; Mantecón, Tomás; Camplani, Massimo; Jaureguizar, Fernando; Salgado, Luis; García, Narciso. 2014. "Foreground Segmentation in Depth Imagery Using Depth and Spatial Dynamic Models for Video Surveillance Applications." Sensors 14, no. 2: 1961-1987.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert