Velocity and stroke rate reconstruction of canoe sprint team boats based on panned and zoomed video recordings
This work provides coaches with an automated, highly accurate tool for performance analysis in canoe sprint, eliminating the need for on-boat sensors or manual annotation.
This paper developed a video-based system to reconstruct velocity and stroke rate for canoe sprint team boats from panned and zoomed video recordings. The system achieved a velocity RRMSE of 0.020 and a stroke rate RRMSE of 0.022 when compared against GPS data from elite competitions.
Pacing strategies, defined by velocity and stroke rate profiles, are essential for peak performance in canoe sprint. While GPS is the gold standard for analysis, its limited availability necessitates automated video-based solutions. This paper presents an extended framework for reconstructing performance metrics from panned and zoomed video recordings across all sprint disciplines (K1-K4, C1-C2) and distances (200m-500m). Our method utilizes YOLOv8 for buoy and athlete detection, leveraging the known buoy grid to estimate homographies. We generalized the estimation of the boat position by means of learning a boat-specific athlete offset using a U-net based boat tip calibration. Further, we implement a robust tracking scheme using optical flow to adapt to multi-athlete boat types. Finally, we introduce methods to extract stroke rate information from either pose estimations or the athlete bounding boxes themselves. Evaluation against GPS data from elite competitions yields a velocity RRMSE of 0.020 +- 0.011 (rho = 0.956) and a stroke rate RRMSE of 0.022 +- 0.024 (rho = 0.932). The methods provide coaches with highly accurate, automated feedback without requiring on-boat sensors or manual annotation.