@Article{CristinaPalmero2016,
author="Cristina Palmero
and Albert Clapes
and Chris Bahnsen
and Andreas M{\o}gelmose
and Thomas B. Moeslund
and Sergio Escalera",
title="Multi-modal RGB-Depth-Thermal Human Body Segmentation",
journal="International Journal of Computer Vision",
year="2016",
publisher="Springer US",
volume="118",
number="2",
pages="217--239",
optkeywords="Human body segmentation",
optkeywords="RGB",
optkeywords="Depth Thermal",
abstract="This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB--depth--thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. The baseline, using Gaussian mixture models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75 \% on the novel dataset when compared to the manually annotated ground-truth of human segmentations.",
optnote="HuPBA;MILAB;",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2767), last updated on Tue, 21 Nov 2017 12:12:27 +0100",
doi="10.1007/s11263-016-0901-x",
file=":http://refbase.cvc.uab.es/files/PCB2016.pdf:PDF"
}