cs.CVFeb 26, 2026
SaveVelocity and stroke rate reconstruction of canoe sprint team boats based on panned and zoomed video recordings
Organizations: Laboratory for Biosignal Processing, Leipzig University of Applied Sciences, Leipzig, Germany · Research Group Canoeing, Institute for Applied Training Science (IAT), Leipzig, Germany · German Canoe Federation, Duisburg, Germany
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 MAPE of 0.011 [0.008 0.014] (Spearman rho=0.974) and a stroke rate MAPE of 0.009 [0.006 0.013] (Spearman rho = 0.975). The methods provide coaches with highly accurate, automated feedback with minimal manual initialization work required, and without requiring sensors.
Figures & tables
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. 3.1 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 and track segments . 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.
| Distance | K1 | C1 | K2 | C2 | K4 | Total |
| 200 m | 8 | 3 | – | – | – | 11 |
| 500 m | 21 | 6 | 7 | 5 | 4 | 43 |
| Total | 29 | 9 | 7 | 5 | 4 | 54 |
Table 1: Contents of our main dataset.
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.
Figure 6: Visualization of stroke estimation methods across one paddle stroke. Top: A motion signal is built by calculating the Euclidean distance between the detected shoulder joint (orange) and wrist joint (blue) normalized by bounding box width in every frame. Bottom: The mean brightness of a patch 20 % the width and height of the athlete bounding box (purple) in the left bottom corner is used as the motion signal.
| Pixel Error (px) | Accuracy (Acc@ ) [%] | |||
| Mean | Median | 1 px | 3 px | 5 px |
| 2.97 | 1.72 | 23.5 | 81.6 | 90.8 |
Table 2: Boat tip detection evaluation on test samples.
Figure 7: Perspective variance of the athlete positioning model relative to race distance. While a minor systematic negative shift in mean error occurs as the camera panning angle increases, the mean absolute error remains below 0.4 m. Standard deviation is consistent. The model maintains sufficient accuracy for boat tip estimation. Best viewed on screen.
| Velocity Metrics (Median [Q1 Q3]) | ||||
| Method | RMSE (m/s) | RRMSE | MAPE | Spearman |
| Linear Interpolation | 0.444 [0.143 0.742] | 0.073 [0.029 0.150] | 0.053 [0.025 0.077] | 0.915 [0.882 0.944] |
| Opt. Flow Athlete Ordering | 0.433 [0.135 0.593] | 0.073 [0.026 0.107] | 0.053 [0.020 0.081] | 0.933 [0.816 0.945] |
| Opt. Flow Interp., causal | 0.129 [0.063 0.170] | 0.022 [0.011 0.036] | 0.013 [0.007 0.022] | 0.968 [0.839 0.980] |
| Opt. Flow Interp., non-causal | 0.095 [0.061 0.136] | 0.016 [0.011 0.023] | 0.010 [0.009 0.012] | 0.982 [0.963 0.992] |
Table 3: Robust multi-athlete tracking ablation. Details see text.
| Velocity Metrics (Median [Q1 Q3]) | ||||
| Boat Type | RMSE (m/s) | RRMSE | MAPE | Spearman |
| K1/C1 | 0.079 [0.063 0.112] | 0.017 [0.014 0.023] | 0.011 [0.008 0.013] | 0.975 [0.960 0.985] |
| K2/C2 | 0.070 [0.048 0.155] | 0.015 [0.009 0.033] | 0.011 [0.006 0.017] | 0.965 [0.924 0.975] |
| K4 | 0.095 [0.061 0.136] | 0.016 [0.011 0.023] | 0.010 [0.009 0.012] | 0.982 [0.963 0.992] |
| All | 0.078 [0.061 0.117] | 0.017 [0.013 0.024] | 0.011 [0.008 0.014] | 0.974 [0.949 0.984] |
Table 4: Correlation and error measures for velocity profiles. Details see text.
Figure 8: Agreement analysis between the proposed velocity estimates and GPS ground truth. Left: Bland-Altman plot illustrating the median difference and limits of agreement (red, 2.5th and 97.5th percentiles), indicating minimal bias and only few outliers beyond the confidence bounds. Shaded area shows the confidence intervals of the limits of agreement, cmp. Sec. 3.9.7 . Right: Relative velocity error over the full dataset, demonstrating low deviation from GPS measurements across the entire velocity range.
Figure 9: Results of GPS-based (blue) and video-based (orange) velocity profiles visualized over race distance. Left: 500m. Right: 200m. Both figures underpin the high agreement between video- and GPS-based measurements.
| Stroke Rate (Median [Q1 Q3]) | ||||
| Method | RMSE (bpm) | RRMSE | MAPE | Spearman |
| ViTPose | 1.753 [0.868 2.921] | 0.014 [0.009 0.022] | 0.009 [0.006 0.013] | 0.975 [0.921 0.990] |
| BBox | 6.467 [4.399 10.031] | 0.070 [0.039 0.088] | 0.039 [0.019 0.053] | 0.807 [0.590 0.914] |
Table 5: Performance metrics and computational efficiency for stroke rate estimation.
Figure 10: Agreement analysis between ViTPose stroke rate estimates and Gyrometric ground truth. Left: Bland-Altman plot illustrating the median difference and limits of agreement (2.5th and 97.5th percentiles), indicating minimal bias and only few outliers beyond the confidence bounds. Shaded area shows the confidence intervals of the limits of agreement, cmp. Sec. 3.9.7 . The two distinct clusters are caused by the substantially different stroke rates of canoe and kayak athletes. Right: Relative stroke rate error over the full dataset, demonstrating low deviation from Gyrometric measurements across the entire stroke rat range. The error range slightly varies over distance which can be attributed to occlusions that consistently occurred (cmp. Fig. 11 ) in the stroke rate validation dataset.
Figure 11: Stroke rate analysis was conducted over all boat classes, but data for multi-athlete boats consistently suffered from occlusions due to several poles that become visible while moving the camera to follow the boats. The occlusion occurred at around 120m (left) and 320m (right) race distance. These correspond to the higher standard deviations observed in our predictions at -380m and -180m, see Fig. 10 .
Figure 12: Results of both Pose-based (green) and Bounding Box based (orange) stroke rate estimation, visualized for two races. The left race shows both methods performing well, as they are both able to capture the form of the profile. On the right, both methods show weaknesses and do not correlate exactly with ground truth data. However, the bounding box method delivers much worse results.
Figure 13: Demonstration of the application of the methodological extensions to analyse team boats as presented in this paper to a K4 canoe race over 500m distance. See. Fig. 1 for annotations. Best viewed on screen.
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