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Human pose estimation, particularly in athletes, can help improve theirperformance. However, this estimation is difficult using existing methods, suchas human annotation, if the subjects wear loose-fitting clothes such asski/snowboard wears. This study developed a method for obtaining the groundtruth data on two-dimensional (2D) poses of a human wearing loose-fittingclothes. This method uses fast-flushing light-emitting diodes (LEDs). Thesubjects were required to wear loose-fitting clothes and place the LED on thetarget joints. The LEDs were observed directly using a camera by selecting thinfilmy loose-fitting clothes. The proposed method captures the scene at 240 fpsby using a high-frame-rate camera and renders two 30 fps image sequences byextracting LED-on and -off frames. The temporal differences between the twovideo sequences can be ignored, considering the speed of human motion. TheLED-on video was used to manually annotate the joints and thus obtain theground truth data. Additionally, the LED-off video, equivalent to a standardvideo at 30 fps, confirmed the accuracy of existing machine learning-basedmethods and manual annotations. Experiments demonstrated that the proposedmethod can obtain ground truth data for standard RGB videos. Further, it wasrevealed that neither manual annotation nor the state-of-the-art pose estimatorobtains the correct position of target joints.

Pose Estimation for Human Wearing Loose-Fitting Clothes: Obtaining Ground Truth Posture Using HFR Camera and Blinking LEDs