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.
2013 Ieee Conference on Computer Vision and Pattern Recognition Workshops (cvprw): Cvprw 2013, 2013, p. 301-307
Main Research Area:
Perception Beyond the Visible SpectrumComputer Vision and Pattern Recognition, 2013