%0 Journal Article
%T Multi-modal RGB-Depth-Thermal Human Body Segmentation
%A Cristina Palmero
%A Albert Clapes
%A Chris Bahnsen
%A Andreas Møgelmose
%A Thomas B. Moeslund
%A Sergio Escalera
%J International Journal of Computer Vision
%D 2016
%V 118
%N 2
%I Springer US
%F Cristina Palmero2016
%O HuPBA;MILAB;
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2767), last updated on Tue, 21 Nov 2017 12:12:27 +0100
%X 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.
%K Human body segmentation
%K RGB
%K Depth Thermal
%U http://refbase.cvc.uab.es/files/PCB2016.pdf
%U http://dx.doi.org/10.1007/s11263-016-0901-x
%P 217-239