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

Andreas Møgelmose, Chris Bahnsen, Thomas B. Moeslund, Albert Clapés, Sergio Escalera

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

68 Citations (Scopus)
512 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) : CVPRW 2013
Number of pages7
Publication date2013
ISBN (Print)9781479909940
ISBN (Electronic)978-0-7695-4990-3
Publication statusPublished - 2013
EventPerception Beyond the Visible Spectrum - Portland, United States
Duration: 24 Jun 201324 Jun 2013
Conference number: 9


WorkshopPerception Beyond the Visible Spectrum
Country/TerritoryUnited States


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