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

TL;DR

This paper analyzes the use of X3D neural networks for estimating ultra-distance runners' performance in wild environments, demonstrating high accuracy with reduced memory usage.

Contribution

It introduces a transfer learning approach using X3D networks for action quality assessment in ultra-distance sports, achieving state-of-the-art results with lower memory requirements.

Findings

Mean absolute error of 12.5 minutes in CRT estimation

X3D outperforms previous models in accuracy

Requires seven times less memory than prior methods

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.