深層学習による動画像処理を用いた索状柔軟物のマーカレス姿勢推定

抄録

In this study, purpose is pose estimation of flexible objects. The object is a cord-like flexible object, and moving image analysis using deep learning is used. The method proposed is markerless pose estimation. The movement of flexible objects is captured as a video and labeled on the image. It learns from labeled images and estimates labeling points. A curve is calculated from that point, and the pose is estimated. In this paper, we actually photographed the movement of a flexible cord-like object, and compared the estimation result with the actual photograph for verification. It was shown that a posture similar to the actual posture can be estimated. The maximum tip error was 2 cm.