Tri-modal Person Re-identification with RGB, Depth and Thermal Features

Andreas Mogelmose, Chris Bahnsen, Thomas B. Moeslund, Albert Clapes, Sergio Escalera; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 301-307

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Mogelmose_2013_CVPR_Workshops,
author = {Mogelmose, Andreas and Bahnsen, Chris and Moeslund, Thomas B. and Clapes, Albert and Escalera, Sergio},
title = {Tri-modal Person Re-identification with RGB, Depth and Thermal Features},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2013}
}