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Title: An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment
Authors: Freire-Obregón, David 
Lorenzo-Navarro, Javier 
Santana Jaria, Oliverio Jesús 
Hernández-Sosa, Daniel 
Castrillón-Santana, Modesto 
UNESCO Clasification: 1203 Ciencia de los ordenadores
3304 Tecnología de los ordenadores
Keywords: Measurement
Machine vision
Neural networks
Transfer learning
Memory management, et al
Issue Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE) 
Project: Re-identificación mUltimodal de participaNtes en competiciones dEpoRtivaS 
Interaccióny Re-Identificación de Personas Mediante Machine Learning, Deep Learningy Análisis de Datos Multimodal: Hacia Una Comunicación Más Natural en la Robótica Social 
Journal: Proceedings Of Mva 2023 - 18Th International Conference On Machine Vision And Applications
Conference: 18th International Conference on Machine Vision Application (MVA 2023)
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.
ISBN: 978-4-88552-343-4
DOI: 10.23919/MVA57639.2023.10215918
Source: 18th International Conference on Machine Vision Applications (MVA) Hamamatsu, Japan, July 23-25, 2023
Appears in Collections:Actas de congresos
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