Towards AI-Powered AR for Enhancing Sports Playability for People with Low Vision: An Exploration of ARSports

People with low vision (LV) experience challenges in visually tracking balls and players in sports like basketball and tennis, which can adversely impact their participation and health. We introduce <i>ARSports</i>, a wearable AR research prototype that overlays instance segmentation masks in near real-time for improving sports accessibility. To create ARSports, we manually collected and annotated novel first-person perspective sports datasets, fine-tuned instance segmentation models using this labeled data, and built an initial wearable AR prototype by combining the <i>ZED Mini</i> stereo camera with the <i>Oculus Quest 2</i> VR headset. Our evaluations suggest that combining real-time computer vision and augmented reality to create scene-aware visual augmentations is a promising approach to enhancing sports participation for LV individuals. We contribute open-sourced egocentric basketball and tennis datasets and models, as well as insights and design recommendations from our pilot study with an LV research team member.