Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis
This work addresses a domain-specific problem for sports analysts and coaches in canoe sprint, but it is incremental as it applies existing methods like Mask R-CNN to a new application area.
The paper tackled the problem of automated waterline detection in canoe sprint video analysis to assess athlete performance, achieving high performance in accordance with expert annotations.
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.