An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment

Abstract

We present a transfer learning analysis on a sporting environment of the expanded 3D (X3D) neural networks. Inspired by action quality assessment methods in the literature, our method uses an action recognition network to estimate athletes' cumulative race time (CRT) during an ultra-distance competition. We evaluate the performance considering the X3D, a family of action recognition networks that expand a small 2D image classification architecture along multiple network axes, including space, time, width, and depth. We demonstrate that the resulting neural network can provide remarkable performance for short input footage, with a mean absolute error of 12 minutes and a half when estimating the CRT for runners who have been active from 8 to 20 hours. Our most significant discovery is that X3D achieves state-of-the-art performance while requiring almost seven times less memory to achieve better precision than previous work.

Accepted to the 18th International Conference on Machine Vision Applications (MVA 2023)

References in corpus (4)

• The Kinetics Human Action Video Dataset
• A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions
• A Survey of Video-based Action Quality Assessment
• Towards cumulative race time regression in sports: I3D ConvNet transfer learning in ultra-distance running events