Automated Identification of Trampoline Skills Using Computer Vision Extracted Pose Estimation

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Title: Automated Identification of Trampoline Skills Using Computer Vision Extracted Pose Estimation
Authors: Connolly, Paul W.
Silvestre, Guenole C.
Bleakley, Chris J.
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Date: 1-Sep-2017
Abstract: 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.
Type of material: Conference Publication
Publisher: Irish Pattern Recognition and Classification Society (IPRCS)
Language: en
Status of Item: Peer reviewed
Conference Details: Irish Machine Vision and Image Processing Conference (IMVIP) 2017, Maynooth University, Ireland, 31 August -1 September 2017
Appears in Collections:Computer Science Research Collection

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