Suranjan Goswami

These are tight pedestrian masks for the thermal images present in the KAIST Multispectral pedestrian dataset, available at https://soonminhwang.github.io/rgbt-ped-detection/

Both the thermal images themselves as well as the original annotations are a part of the parent dataset. Using the annotation files provided by the authors, we develop the binary segmentation masks for the pedestrians, using the Segment Anything Model from Meta.

All masks are present as grayscale binary png files, having pixel values 255 (for the relevant masks) and 0 (everything else).

We present here an annotated thermal dataset which is linked to the dataset present in https://ieee-dataport.org/open-access/thermal-visual-paired-dataset

To our knowledge, this is the only public dataset at present, which has multi class annotation on thermal images, comprised of 5 different classes.

This database was hand annotated over a period of 130 work hours.

This is a dataset having paired thermal-visual images collected over 1.5 years from different locations in Chitrakoot, India and Prayagraj, India. The images can be broadly classified into greenery, urban, historical buildings and crowd data.

The crowd data was collected from the Maha Kumbh Mela 2019, Prayagraj, which is the largest religious fair in the world and is held every 6 years.