Decontextualized I3D ConvNet for ultra-distance runners performance analysis at a glance

TL;DR

This paper explores using an I3D ConvNet to analyze ultra-distance runners' gait from non-invasive footage, aiming to predict performance despite environmental challenges, contributing a novel gait-based performance estimation method.

Contribution

It introduces a decontextualized I3D ConvNet approach for ultra-distance runner performance analysis, removing environmental factors to improve prediction accuracy.

Findings

I3D ConvNet features effectively estimate runner performance.

Gait analysis remains robust despite weather and occlusions.

Performance prediction aligns with race outcomes.

Abstract

In May 2021, the site runnersworld.com published that participation in ultra-distance races has increased by 1,676% in the last 23 years. Moreover, nearly 41% of those runners participate in more than one race per year. The development of wearable devices has undoubtedly contributed to motivating participants by providing performance measures in real-time. However, we believe there is room for improvement, particularly from the organizers point of view. This work aims to determine how the runners performance can be quantified and predicted by considering a non-invasive technique focusing on the ultra-running scenario. In this sense, participants are captured when they pass through a set of locations placed along the race track. Each footage is considered an input to an I3D ConvNet to extract the participant's running gait in our work. Furthermore, weather and illumination capture conditions or occlusions may affect these footages due to the race staff and other runners. To address this challenging task, we have tracked and codified the participant's running gait at some RPs and removed the context intending to ensure a runner-of-interest proper evaluation. The evaluation suggests that the features extracted by an I3D ConvNet provide enough information to estimate the participant's performance along the different race tracks.