%0 Conference Proceedings
%T Tri-modal Person Re-identification with RGB, Depth and Thermal Features
%A Andreas Møgelmose
%A Chris Bahnsen
%A Thomas B. Moeslund
%A Albert Clapes
%A Sergio Escalera
%B 9th IEEE Workshop on Perception beyond the visible Spectrum, Computer Vision and Pattern Recognition
%D 2013
%@ 978-0-7695-4990-3
%F Andreas Møgelmose2013
%O HUPBA;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2253), last updated on Fri, 11 Jan 2019 12:13:52 +0100
%X Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined classifier. The tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios.
%U http://refbase.cvc.uab.es/files/MBM2013.pdf
%U http://dx.doi.org/10.1109/CVPRW.2013.52
%P 301-307