Determining a waterline in images recorded in canoesprint 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 MaskR-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 ourmethod 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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