PT Unknown
AU Andreas Møgelmose
   Chris Bahnsen
   Thomas B. Moeslund
   Albert Clapes
   Sergio Escalera
TI Tri-modal Person Re-identification with RGB, Depth and Thermal Features
BT 9th IEEE Workshop on Perception beyond the visible Spectrum, Computer Vision and Pattern Recognition
PY 2013
BP 301
EP 307
DI 10.1109/CVPRW.2013.52
AB 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.
ER