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GAIT3: An Event-based, RGB and Thermal Gait Database

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Gesellschaft für Informatik e.V.


Identifying people by their gait has gained popularity in the last twenty years. Recent gait recognition methods use acquisitions extracted from advanced sensors such as cameras, depth sensors, microphones, etc. Recently, event-based cameras, a new family of cameras, are gaining popularity. They are vision sensors that differ completely from conventional cameras: instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes generated by moving objects. This motivated us to use it for individual recognition by gait. In this paper, we provide means for multimodal gait recognition, by introducing the “Event-based, RGB, and Thermal Gait” database. This database is the first that contains event-camera acquisition, simultaneously with conventional RGB and thermal videos. It contains recordings of people in three variations: normal walking, quick walking, and walking with a backpack. We also present experiments using a baseline algorithm based on gait energy images adapted to event-based camera output. Then we present a comparative experiment against RGB and thermal videos, using the same algorithm, that shows an advantage for event-based data.


Mohamed Eddine and Jean-Luc Dugelay (2022): GAIT3: An Event-based, RGB and Thermal Gait Database. BIOSIG 2022. DOI: 10.1109/BIOSIG55365.2022.9897039. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5497. ISBN: 978-3-88579-723-4. pp. 285-292. Further Conference Contributions. Darmstadt. 14.-16. September 2022