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Title: A large-scale analysis of athletes’ cumulative race time in running events
Authors: Freire Obregón, David Sebastián 
Lorenzo Navarro, José Javier 
Santana Jaria, Oliverio Jesús 
Hernández Sosa, José Daniel 
Castrillón Santana, Modesto Fernando 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Sports Analytics
Ultra-distance competition
Human Action Recognition
Issue Date: 2023
Publisher: Springer 
Project: Interaccióny Re-Identificación de Personas Mediante Machine Learning, Deep Learningy Análisis de Datos Multimodal: Hacia Una Comunicación Más Natural en la Robótica Social 
Journal: Lecture Notes in Computer Science 
Conference: 22th International Conference on Image Analysis and Processing (ICIAP 2023)
Abstract: Action recognition models and cumulative race time (CRT) are practical tools in sports analytics, providing insights into athlete performance, training, and strategy. Measuring CRT allows for identifying areas for improvement, such as specific sections of a racecourse or the effectiveness of different strategies. Human action recognition (HAR) algorithms can help to optimize performance, with machine learning and artificial intelligence providing real-time feedback to athletes. This paper presents a comparative study of HAR algorithms for CRT regression, examining two important factors: the frame rate and the regressor selection. Our results indicate that our proposal exhibits outstanding performance for short input footage, achieving a mean absolute error of 11 min when estimating CRT for runners that have been on the course for durations ranging from 8 to 20 h.
ISBN: 978-3-031-43147-0
ISSN: 0302-9743
DOI: 10.1007/978-3-031-43148-7_24
Source: ICIAP 2023: Image Analysis and Processing. Lecture Notes in Computer Science, [ISSN 0302-9743], vol 14233, p. 282–292 (September,2023)
Appears in Collections:Actas de congresos
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