OpenThermalPose2

Human pose estimation has applications in numerous fields, including action recognition, human-robot interaction, motion capture, augmented reality, sports analytics, and healthcare. Many datasets and deep learning models are available for human pose estimation within the visible domain. However, challenges such as poor lighting and privacy issues persist. These challenges can be addressed using thermal cameras; nonetheless, only a few annotated thermal human pose datasets are available for training deep learning-based human pose estimation models. In this regard, we introduce a novel open-source thermal human pose dataset named OpenThermalPose2. The dataset contains 11,391 thermal images of 170 subjects and 21,125 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.