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