  A novel method to identify trampoline skills using a single video camera is
proposed herein. Conventional computer vision techniques are used for
identification, estimation, and tracking of the gymnast's body in a video
recording of the routine. For each frame, an open source convolutional neural
network is used to estimate the pose of the athlete's body. Body orientation
and joint angle estimates are extracted from these pose estimates. The
trajectories of these angle estimates over time are compared with those of
labelled reference skills. A nearest neighbour classifier utilising a mean
squared error distance metric is used to identify the skill performed. A
dataset containing 714 skill examples with 20 distinct skills performed by
adult male and female gymnasts was recorded and used for evaluation of the
system. The system was found to achieve a skill identification accuracy of
80.7% for the dataset.
