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Only using RGB cameras for automatic outdoor scene analysis is challenging when, for example, facing insufficient illumination or adverse weather. To improve the recognition reliability, multispectral systems add additional cameras (e.g. infra-red) and perform object detection from multispectral data. Although multispectral scene analysis with deep learning has be shown to have a great potential, there are still many open research questions and it has not been widely deployed in industrial contexts.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
KAIST Multispectral Pedestrian Dataset The KAIST Multispectral Pedestrian Dataset is imaging hardware consisting of a color camera, a thermal camera and a beam splitter to capture the aligned multispectral (RGB color + Thermal) images. With this hardware, we captured various regular traffic scenes at day and night time to consider changes in light conditions. and, consists of 95k color-thermal pairs (640x480, 20Hz) taken from a vehicle. All the pairs are manually annotated (person, people, cyclist) for the total of 103,128 dense annotations and 1,182 unique pedestrians. The annotation includes temporal correspondence between bounding boxes like Caltech Pedestrian Dataset. For more information, read Multispectral Pedestrian Detection: Benchmark Dataset and Baseline (CVPR 2015) or visit this website
The National Institute of Informatics - Chiba University (NII-CU) Multispectral Aerial Person Detection Dataset consists of 5,880 pairs of aligned RGB+FIR (Far infrared) images captured from a drone flying at heights between 20 and 50 meters, with the cameras pointed at 45 degrees down. We applied lens distortion correction and a homography warping to align the thermal images with the RGB images. We then labeled the people visible on the images with rectangular bounding boxes. The footage shows a baseball field and surroundings in Chiba, Japan, recorded in January 2020. *RGB images captured with a Zenmuse X3 (3840x2160) *Thermal images captured with a FLIR Vue Pro 640 in White Hot palette (640x512 before alignment) *RGB images are corrected for lens distortion *Thermal images are corrected for lens distortion and warped to be aligned with RGB images *Labels are provided in CSV format
The MS-EVS Dataset is the first large-scale event-based dataset for face detection. - Regular images (APS) and simulated events (EVS) - Multispectral data (RGB and/or near IR) - Very diverse faces and data quality (for robust face detection) It is divided in four subsets: - N-YoutubeFaces, 3k+ celebrity faces from Youtube videos, in color (simulated events). - N-MobiFace, 80 unedited mobile live color videos (simulated events). - N-SpectralFace, 69 multispectral videos captured in-house (simulated events). - Real-SpectralFace, a few sequences with real multispectral events (Grayscale and Infrared). | Dataset | APS | Simulated EVS | Real EVS | Data | |:---------------------:|:-------:|:-----------------:|:------------:|:--------------------------------------:| | N-YoutubeFaces | x | x | | Color (RGB) | | N-MobiFace | x | x | | Color (RGB) | | N-SpectralFace | x | x | | Multispectral (9 visible + 1 infrared) | | Real-SpectralFace | x | | x | Multispectral (1 visible + 1 infrared) |
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