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

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, mor...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2018-03, Vol.19 (3), p.934-948
Main Authors: Choi, Yukyung, Kim, Namil, Hwang, Soonmin, Park, Kibaek, Yoon, Jae Shin, An, Kyounghwan, Kweon, In So
Format: Article
Language:English
Subjects:
advanced driver assistance system
autonomous driving
benchmarks
Calibration
Cameras
Coarsening
Dataset
Datasets
Image detection
Image enhancement
Inertial sensing devices
KAIST multi-sepctral
Laser radar
Lidar
multi-spectral dataset in day and night
multi-spectral vehicle system
Night
Object recognition
Optical distortion
Optical imaging
Optical sensors
Spectra
Sunrise
Sunset
Thermal imaging
Thermal sensors
Toolkits
Visual perception
Visual tasks
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2791533