TrackAid-DT: An egocentric vision dataset and benchmarks for lane detection in visually impaired athlete guidance

Running on marked tracks is central to athletic training, still athletes with visual impairment depend on external guides limiting independence in training and competition. Advancements in assistive and navigation technologies offer new possibilities for inclusive sports environments. Therefore, there is an urgent need to develop inclusive mobility solutions that empower visually impaired athletes. In this work, we focus on an autonomous lane guidance system designed to assist athletes in independent navigation. To address this investigation, we propose an end-to-end vision-based system built upon our TrackAid Dataset (TrackAid-DT). The system operates under real-world conditions and supports safe athletic running by delivering vibrotactile haptic feedback for real-time guidance, in contrast to prior works that primarily address walking or jogging. TrackAid-DT comprises egocentric, pixel-annotated monocular images acquired from chest-mounted cameras under diverse orientations, speeds, and lighting conditions, and is designed to train segmentation models that enable the proposed guidance system and its deployment on edge devices. We benchmark multiple advanced segmentation models using eight-fold cross validation, task specific pretraining with public KITTI dataset and report segmentation accuracy together with computational efficiency on both training and generalization sets. Among the considered models, pretrained TransUNet achieved the strongest segmentation quality (IoU 0.929± 0.064, F1 0.962± 0.036), while U-Net offered a more deployable balance (IoU 0.921± 0.064, 80.31 ms/frame, 12.45 FPS, and the lowest environmental impact at 16.49 g CO2e). We consider the trade-offs and deploy the system on multiple edge devices and validate it through outdoor prototype testing showing its practical effectiveness in real-world running scenarios. The system maintains stable lane detection and delivers timely haptic feedback at speeds of up to 10 km/h, demonstrating feasibility for autonomous vision based guidance in athletic running. To foster further development in this domain all code and data are publicly released (link: https://github.com/vrai-group/TrackAid-DT ).