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

By Yukyung Choi, Namil Kim, Soonmin Hwang, Kibaek Park, Jae Shin Yoon, Kyounghwan An, In-So Kweon. 2018

348 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “KAIST multi-spectral [283] X X X Vehicle X”
nuScenes: A Multimodal Dataset for Autonomous Driving (2019, methodology): “The KAIST multispectral dataset [17] is a multimodal dataset that consists of RGB and thermal camera, RGB stereo, 3D lidar and GPS/IMU.”
Canadian Adverse Driving Conditions dataset (2020, methodology): “In 2018, two new datasets were released, the ApolloScape Open Dataset [3] and the KAIST dataset [4].”
First-Person View Hand Segmentation of Multi-Modal Hand Activity Video Dataset (2020, background): “Multi-modal data is widely used to capture robust features because of color and texture invariance and its expression of an object in an explicit 3D information [15, 17, 37, 52].”
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting (2023, background): “Datasets exist with unique modalities such as thermal imagery [10, 9].”
3D Object Detection for Autonomous Driving: A Comprehensive Survey (2022, background): “5 Yes 15k 15k 200k 8 No/No Germany KAIST [51] 2018 - Yes 8.”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, methodology): “The KAIST Multispectral Dataset [92] provides both thermal images and LiDAR data.”
Drone-Based RGB-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning (2020, background): “However, most of these meth- 42 ods were designed for the RGB modality alone, and cannot 43 overcome the challenges in complex scenarios, such as low- 44 light conditions [15], [16].”
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation (2019, methodology): “tention in the literature due to its ubiquitous nature. Several works pertain to human detection at nighttime, using FIR cameras [14], [15], visible light cameras [16], or a combination of both [17], [18]. In driving scenarios, a few methods have been proposed to detect cars [19] and vehicles’ rear ”
One Million Scenes for Autonomous Driving: ONCE Dataset (2021, methodology): “The KAIST Multi-Spectral dataset [11] uses thermal imaging cameras to record scenes.”
TartanAir: A Dataset to Push the Limits of Visual SLAM (2020, background): “KAIST introduces a multispectral dataset covering day/night cases for autonomous and assisted driving [17].”