Thermal Infrared Pedestrian Detection

• Visible-infrared Paired Dataset for Low-light Vision * 30976 images (15488 pairs) * 24 dark scenes, 2 daytime scenes * Support for image-to-image translation (visible to infrared, or infrared to visible), visible and infrared image fusion, low-light pedestrian detection, and infrared pedestrian detection * (The original image and video pairs (before registration) of LLVIP are also released!)

InfraParis is a novel and versatile dataset supporting multiple tasks across three modalities: RGB, depth, and infrared. From the city to the suburbs, it contains a variety of styles in different areas of the greater Paris area, providing rich semantic information. InfraParis contains 7301 images with bounding boxes and full semantic (19 classes) annotations. We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation.

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