Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Camplani, Massimo and Salgado Álvarez de Sotomayor, Luis (2014). Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers. "Journal of Visual Communication and Image Representation", v. 25 (n. 1); pp. 122-136. ISSN 1047-3203. https://doi.org/10.1016/j.jvcir.2013.03.009.

Description

Title: Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers
Author/s:
  • Camplani, Massimo
  • Salgado Álvarez de Sotomayor, Luis
Item Type: Article
Título de Revista/Publicación: Journal of Visual Communication and Image Representation
Date: January 2014
ISSN: 1047-3203
Volume: 25
Subjects:
Freetext Keywords: RGB-D cameras; Microsoft Kinect; Background/foreground segmentation; Combination of classifiers; RGB-D dataset; Mixture of Gaussian; Color and depth data combination; Object detection
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 RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author’s knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2010-20412UnspecifiedUnspecifiedUnspecified

More information

Item ID: 37450
DC Identifier: http://oa.upm.es/37450/
OAI Identifier: oai:oa.upm.es:37450
DOI: 10.1016/j.jvcir.2013.03.009
Official URL: http://www.sciencedirect.com/science/article/pii/S1047320313000497
Deposited by: Memoria Investigacion
Deposited on: 12 Sep 2015 07:56
Last Modified: 01 Feb 2016 23:56
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