Swimmer position estimation and stroke analysis based on head recognition
Mayuko Ishikawa, Hidehiko Shishido, Yoshinari Kameda · International Workshop on Advanced Imaging Technology (IWAIT) 2022 · 2022
In the field of competitive swimming, performance investigation on official games is important and useful for performance development. We have been working on swimmer position estimation from a wide video view of official games. The challenges are water splash and complicated reflection of light that may hide swimmers from the camera. To overcome these problems, we utilize YOLOv3 and prepare a dedicated dataset of swimmers' heads in real games. We have trained the YOLOv3, and the trained YOLOv3 can detect heads by 48.1% mAP. In addition to the position estimation, we also propose a new method to investigate the status of the strokes along the time by detecting two head-classes: over the water and under the water. We also prepare another dedicated dataset for this two-class training. With the trained YOLOv3, we successfully visualize the status change of a swimmer over a whole game.