Velocity and stroke rate reconstruction of canoe sprint team boats based on panned and zoomed video recordings

Abstract

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.

Figures (13)

• Figure 1: Visualization of the reconstructed scene geometry for a K1 canoe race over the distance of 500 m according to matthes_reconstructing_2025. Orange lines correspond to lane boundaries, blue ones to boundaries between equidistant segment, and yellowish dots where orange and blue lines cross are expected buoy location alongside the track. See Sec. \ref{['sec:prevwork']} for an outline of the scene reconstruction method. Numbers in red coloured annotations at boat tips correspond to the distance travelled of each boat, which can further be utilized to reconstruct velocity profiles. Best viewed on screen.
• Figure 2: Shown is the scene geometry of a regatta course (left) and an approximate orthogonal camera view on the scene (right). For clarity, yellow buoys highlighted in the scene are those visible in the right image. Corresponding buoys in both views can be identified based on their lane boundaries $B_i$ and track segments $S_j$. Correspondences are used to estimate a homography and propagated to adjacent frames. Adapted from matthes_reconstructing_2025.
• Figure 3: Visualization of all boat classes in our main dataset. C refers to canoe, where single-bladed paddles are used. K refers to kayak, where double-bladed paddles are mandated. The number indicated the number of athletes per boat.
• Figure 4: Graphical Illustration of Automatic Boat Tip Offset Calibration. (1) For every detected athlete bounding box (green), we assume the centre of the bottom edge to be the image position of that athlete (blue point). (2) Athlete positions are transformed to the 2d world space, and a standard offset is applied to the mean position of the athletes (orange). (3) This position is reprojected into the image, and defined as centre of ROI. (4) ROI is input into a trained U-Net, which predicts the exact tip location (bright green). (5) Exact tip location and athlete positions in the image are again transformed to world coordinates. (6) Individual athlete offsets are calculated (red arrows). Best viewed on screen.
• Figure 5: Principle of boat tip localization using a U-Net. The image annotation (green dot) of the boat tip (left) is encoded via a heatmap with gaussian kernel (middle). The U-Net is trained to predict this heatmap (right) from which the predicted position (red cross) is derived using argmax over the predicted heatmap.