KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving

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We introduce the KAIST multi-spectral data set, which covers a great range of drivable regions, from urban to residential, for autonomous systems. Our data set provides the different perspectives of the world captured in coarse time slots (day and night), in addition to fine time slots (sunrise, morning, afternoon, sunset, night, and dawn). For all-day perception of autonomous systems, we propose the use of a different spectral sensor, i.e., a thermal imaging camera. Toward this goal, we develop a multi-sensor platform, which supports the use of a co-aligned RGB/Thermal camera, RGB stereo, 3-D LiDAR, and inertial sensors (GPS/IMU) and a related calibration technique. We design a wide range of visual perception tasks including the object detection, drivable region detection, localization, image enhancement, depth estimation, and colorization using a single/multi-spectral approach. In this paper, we provide a description of our benchmark with the recording platform, data format, development toolkits, and lessons about the progress of capturing data sets.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-03
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.19, no.3, pp.934 - 948

ISSN
1524-9050
DOI
10.1109/TITS.2018.2791533
URI
http://hdl.handle.net/10203/241204
Appears in Collection
EE-Journal Papers(저널논문)
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