Thermal Image Segmentation

The first RGB-Thermal urban scene image dataset with pixel-level annotation. We published this new RGB-Thermal semantic segmentation dataset in support of further development of autonomous vehicles in the future. This dataset contains 1569 images (820 taken at daytime and 749 taken at nighttime). Eight classes of obstacles commonly encountered during driving (car, person, bike, curve, car stop, guardrail, color cone, and bump) are labeled in this dataset.

PST900 is a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge. Source: https://arxiv.org/abs/1909.10980 Image Source: https://github.com/ShreyasSkandanS/pst900_thermal_rgb

Charlotte-ThermalFace is a thermal face dataset. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. There are approximately 10,000 infrared thermal images from 10 subjects in varying thermal conditions, at several distances from the camera, and at changing head positions. We have also controlled the air temperature to change from 20.5°C ( 69°F) to 26.5 °C( 80°F). Images are available in four different temperatures, 10 relative distances from the camera, starting at 1m ( 3.3 ft) to 6.6m( 21.6 ft), and 25 head positions. • The first public facial thermal dataset annotated with the environmental properties including air temperature, relative humidity, airspeed, distance from the camera, and subjective thermal sensation of each person at the time. • All the images are manually annotated with 72 or 43 facial landmarks. • We are publishing the data in the original 16-bit radiometric TemperatureLinear format, which has the thermal value of each pixel.

Abstract: We introduce the multi-spectral stereo (MS2) outdoor dataset, including stereo RGB, stereo NIR, stereo thermal, stereo LiDAR data, and GPS/IMU information. Our dataset provides rectified and synchronized 184K data pairs taken from city, residential, road, campus, and suburban areas in the morning, daytime, and nighttime under clear-sky, cloudy, and rainy conditions. We designed the dataset to explore various computer vision algorithms from multi-spectral sensor data to achieve high-level performance, reliability, and robustness against challenging environments. MS2 dataset provides: * 1. (Synchronized) Stereo RGB images / Stereo NIR images / Stereo thermal images * 2. (Synchronized) Stereo LiDAR scans / GPS/IMU navigation data * 3. Projected depth map (in RGB, NIR, thermal image planes) * 4. Odometry data (in RGB, NIR, thermal cameras, and LiDAR coordinates)