Identifying Athletics Tracks using Keypoint Detection
Gareth Harcombe, Richard Green, Kourosh Neshatian · 2023
This paper proposes the first known approaches to locating an athletics track from GPS data of a running workout. This is done by mapping the GPS points onto an image and applying Computer Vision methods, including: performing keypoint detection using a Keypoint Region-Based Convolutional Neural Network (R-CNN) model with a Residual Neural Network backbone; and applying Convolutional Kernels over the image. The Keypoint R-CNN model achieved a Root Mean Square Error (RMSE) of 1.29 on synthetic data, which is lower than the error of other keypoint detection models of 2.23, but performed poorly on real data. The Convolutional Method achieved an RMSE of 4.20. The methods and results of this paper can be used in future applications for error correction and feature extraction of GPS data for running workout analysis.