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Computer vision for sports

IAIN Batusangkar, Indonesia

Received: 11 Dec 2021; Published: 30 Apr 2022.
Open Access Copyright (c) 2022 Adriyendi Adriyendi Adriyendi
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

We explore theories and applications of Computer Vision (CV) in sports. We use the method proposed included: object, research question, search process, inclusion and exclusion, quality assessment, data collection, data analysis, and characteristics of the article. We review it based on problem, methods, interpretation, finding, and future work. We analyze it based on categories: recognition, motion, detection, classification, identification, and automation. Process CV in sports included computing technology, capture motion, multi-scenarios, application of statistical sports, output prediction, object measurement, performance, and object adjudication. We found that Machine Learning (ML) and Deep Learning (DL) were widely used on CV in sports. DL approach has more advantages than the ML approach because the DL approach is supported by high-performance computing and high-quality image datasets. The implication of this research is an artificial feature-based, multi-scenarios, syntaxis method, rapid prototype, indoor localization, and gaze method as big challenge and new potential research for CV in sports. 

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Keywords: computer vision; sport; machine learning; deep learning
Funding: -

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