Dronaquatics: Real-time Swimming Analytics Using Drone Captured Imagery

Accurate swimming performance monitoring has traditionally relied on wearable sensors, which can disrupt natural technique and are often impractical in competitive settings. In this paper, we present a fully vision-based system for automatic swimmer analysis using overhead drone footage, removing the need for any body-mounted device or underwater equipment. By fine-tuning pose estimation models for aerial aquatic conditions, our approach robustly extracts full-body swimmer skeletons even under challenging scenarios such as splashes and partial occlusions. From these poses, we classify swimming strokes, compute instantaneous speed, estimate lap times, and count individual strokes. Unlike existing methods, our system provides scalable, unobtrusive, and infrastructure-free tracking. Evaluated on real-world drone-captured swimming competition data, our method achieves a median speed estimation error below 4\% (under 0.05 m/s), a median lap time error of just 0.03s, and stroke count errors typically under one stroke per lap.