Reichert Gijs M W, Pieras Marcos, Marroquim Ricardo, Vilanova Anna
Computer Graphics and Visualization, Technical University Delft, Delft, 2628, CD, The Netherlands.
Visualization Group, Technical University Eindhoven, Eindhoven, AZ, 5612, The Netherlands.
Vis Comput Ind Biomed Art. 2021 Oct 19;4(1):26. doi: 10.1186/s42492-021-00093-x.
One common way to aid coaching and seek to improve athletes' performance is by recording training sessions for posterior analysis. In the case of sailing, coaches record videos from another boat, but usually rely on handheld devices, which may lead to issues with the footage and missing important moments. On the other hand, by autonomously recording the entire session with a fixed camera, the analysis becomes challenging owing to the length of the video and possible stabilization issues. In this work, we aim to facilitate the analysis of such full-session videos by automatically extracting maneuvers and providing a visualization framework to readily locate interesting moments. Moreover, we address issues related to image stability. Finally, an evaluation of the framework points to the benefits of video stabilization in this scenario and an appropriate accuracy of the maneuver detection method.
辅助训练并试图提高运动员表现的一种常见方法是录制训练课程以便事后分析。就帆船运动而言，教练从另一艘船上录制视频，但通常依赖手持设备，这可能会导致画面出现问题并错过重要时刻。另一方面，通过使用固定摄像头自主录制整个训练课程，由于视频长度和可能的稳定性问题，分析变得具有挑战性。在这项工作中，我们旨在通过自动提取动作并提供一个可视化框架来方便地定位有趣时刻，从而促进对此类完整训练课程视频的分析。此外，我们解决与图像稳定性相关的问题。最后，对该框架的评估指出了在这种情况下视频稳定的好处以及动作检测方法的适当准确性。
