Automated Identification of Trampoline Skills Using Computer Vision Extracted Pose Estimation

TL;DR

This paper presents a computer vision-based system that uses pose estimation and machine learning to automatically identify trampoline skills from video recordings, achieving over 80% accuracy.

Contribution

It introduces a novel approach combining pose estimation and nearest neighbor classification for skill identification in trampoline routines.

Findings

Achieved 80.7% accuracy in skill identification

Utilized a dataset of 714 skill examples across 20 skills

Demonstrated effectiveness of pose-based features for skill recognition

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.