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Title: Decontextualized I3D ConvNet for Ultra-Distance Runners Performance Analysis at a Glance
Authors: Freire Obregón, David Sebastián 
Lorenzo Navarro, José Javier 
Castrillón Santana, Modesto Fernando 
UNESCO Clasification: 3304 Tecnología de los ordenadores
Keywords: Human action evaluation
I3D ConvNet
Issue Date: 2022
Publisher: Springer 
Project: Re-identificación mUltimodal de participaNtes en competiciones dEpoRtivaS 
Journal: Lecture Notes in Computer Science 
Conference: 21st International Conference Image Analysis and Processing (ICIAP 2022) 
Abstract: In May 2021, the site 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.
ISBN: 978-3-031-06432-6
ISSN: 0302-9743
DOI: 10.1007/978-3-031-06433-3_21
Source: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, v. 13233
Appears in Collections:Actas de congresos
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