Conference Proceedings


Chris J. Bleakley
Guenole C. Silvestre
Paul W. Connolly


Computer Science

computer vision techniques computer vision image processing pose estimation open source machine vision neural network video recording

Automated Identification of Trampoline Skills Using Computer Vision Extracted Pose Estimation (2017)

Abstract Irish Machine Vision and Image Processing Conference (IMVIP) 2017, Maynooth University, Ireland, 31 August -1 September 2017 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.
Collections Ireland -> University College Dublin -> College of Science
Ireland -> University College Dublin -> Computer Science Research Collection
Ireland -> University College Dublin -> School of Computer Science

Full list of authors on original publication

Chris J. Bleakley, Guenole C. Silvestre, Paul W. Connolly

Experts in our system

Chris Bleakley
University College Dublin
Total Publications: 105
Guenole C. Silvestre
University College Dublin
Total Publications: 3