PT Journal
AU Cristina Palmero
   Albert Clapes
   Chris Bahnsen
   Andreas Møgelmose
   Thomas B. Moeslund
   Sergio Escalera
TI Multi-modal RGB-Depth-Thermal Human Body Segmentation
SO International Journal of Computer Vision
JI IJCV
PY 2016
BP 217
EP 239
VL 118
IS 2
DI 10.1007/s11263-016-0901-x
DE Human body segmentation; RGB; Depth Thermal
AB 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.
ER