Abstract
Human pose estimation has a variety of applications in action recognition, human-robot interaction, motion capture, augmented reality, sports analytics, and healthcare. There is a substantial stream of datasets and deep learning-based models to attain robust human pose estimation within the visible domain. Nonetheless, there are certain obstacles in this domain, including insufficient illumination and privacy concerns. These issues can be addressed using thermal cameras. However, only a limited number of annotated thermal human pose datasets are available to train data-hungry deep learning models. In this regard, we introduce a novel open-source thermal human pose dataset named OpenThermalPose. The dataset contains 6,090 thermal images and 14,315 annotated human instances. The annotations include bounding boxes and 17 anatomical keypoints, following the annotation format of the MS COCO dataset. The dataset covers various fitness exercises, multiple-person activities, and outdoor walking in different locations and weather conditions. As a baseline, we trained and evaluated YOLOv8-pose models on our dataset. We have made the dataset, source code, and pre-trained models publicly available at https://github.com/IS2AI/OpenThermalPose to bolster research in this area.