基于模板R-CNN的皮划艇短跑视频水线检测
Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis
摘要：
摘要在皮划艇短跑训练中，确定运动员的水线位置是运动学参数分析的重要组成部分。在这里，我们提出了一种自动水线检测的方法。首先，我们通过迁移学习使用预训练的Mask R-CNN进行独木舟分割。其次，我们开发了一个多阶段的方法来估计水线的轮廓的部分。它包括两个线性回归阶段和独木舟部件的系统选择。然后，我们引入了水线的参数化，作为进一步评估的基础。接下来，我们在几位专家中进行了一项研究，以估计地面真实水线。这不仅包括从各个专家注释中得出的平均水线，更重要的是，还包括对各个结果之间不确定性的测量。最后，我们评估了我们的方法的问题是否预测的水线是根据专家的注释。我们的方法表现出高性能，并提供了新的应用领域的自动视频分析在独木舟短跑的机会。
英文摘要：
Determining a waterline in images recorded in canoe sprint training is an important component for the kinematic parameter analysis to assess an athlete's performance. Here, we propose an approach for the automated waterline detection. First, we utilized a pre-trained Mask R-CNN by means of transfer learning for canoe segmentation. Second, we developed a multi-stage approach to estimate a waterline from the outline of the segments. It consists of two linear regression stages and the systematic selection of canoe parts. We then introduced a parameterization of the waterline as a basis for further evaluations. Next, we conducted a study among several experts to estimate the ground truth waterlines. This not only included an average waterline drawn from the individual experts annotations but, more importantly, a measure for the uncertainty between individual results. Finally, we assessed our method with respect to the question whether the predicted waterlines are in accordance with the experts annotations. Our method demonstrated a high performance and provides opportunities for new applications in the field of automated video analysis in canoe sprint.
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