KAIST Multi-spectral Recognition Dataset in day and night

This paper presents all-day dataset of paired a multi-spectral 2d vision (RGB-Thermal and RGB stereo) and 3d lidar (Velodyne 32E) data collected in campus and urban environments. Over all days, we successfully captured 50km sequences of synchronized multiple sensors at 25Hz using a fully aligned visible and thermal device, high resolution stereo visible cameras, and high accuracy GPS/IMU inertial navigation system. Therefore, this dataset contains various illumination conditions (day, night, sunset, and sunrise) of multimodal data, which are of particular interest in autonomous driving-assistance tasks such as localization (place recognition, 6D SLAM), moving object detection (pedestrian or car) and scene understanding (drivable region). In this paper, we provide the instruction of our dataset, including a recoding platform, the data format and the software for MATLAB and C++, demonstrating how to load and use the dataset.

Draft Paper

• [Draft]

Related Papers (using this dataset)

• [Report] Multispectral Transfer Network: the shining in the night
• [Report] A New Perspective on Thermal Imaging Enhancement using Convolution Neural Network
• [IROS2016] Thermal Image Enhancement using Convolution Neural Network
• [IV2016] Thermal-Infrared based Drivable Region Detection
• [URAI2015] Geometrical Calibration of Multispectral Calibration
• [URAI2015] Low-cost Synchronization for Multispectral Cameras
• [CVPR2015] Multispectral Pedestrian Detection: Benchmark Dataset and Baseline
• [CVPRW2015] All-Day Visual Place Recognition: Benchmark Dataset and Baseline

Data & software download

• Thumbnail data
• [Raw data]
• [Rectified data]
• [Devlopment kits]
• [Annotation kits]

Supplementary

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Contact

Please contact Yukyung Choi with questions and comments.

Website Log

01.30.2016: The "KAIST Multi-spectral Recognition Dataset in day and night" webpages beta test (not public open)