论文标题
在独木舟冲刺视频分析中利用面具R-CNN进行水线检测
Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis
论文作者
论文摘要
确定在独木舟冲刺训练中记录的图像中的水线是评估运动员表现的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，我们通过独木舟分割的转移学习利用了预训练的面膜R-CNN。其次，我们开发了一种多阶段的方法来估算细分市场轮廓的水线。它由两个线性回归阶段和独木舟零件的系统选择组成。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，我们对几位专家进行了一项研究，以估算地面真相水线。这不仅包括从单个专家注释中得出的平均水线，而且更重要的是，衡量了个体结果之间的不确定性。最后，我们在问题上评估了我们的方法是否符合专家注释。我们的方法证明了高性能，并为独木舟冲刺的自动视频分析领域提供了新应用的机会。
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.
