DOI: https://doi.org/10.14710/jtsiskom.2022.14373

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.

Citation Format:
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. 

Fulltext Email colleagues
Keywords: computer vision; sport; machine learning; deep learning
Funding: -

Article Metrics:

Article Info
Section: Review Articles
Language : ID
Related articles
Grape leaf image disease classification using CNN-VGG16 model Prediksi Kedatangan Turis Asing ke Indonesia Menggunakan Backpropagation Neural Networks Predictive Adaptive Test with Selective Weighted Bayesian Through Questions and Answers Patterns to Measure Student Competency Levels Sequence-based prediction of protein-protein interaction using autocorrelation features and machine learning Three combination value of extraction features on GLCM for detecting pothole and asphalt road More related articles
Most cited articles
Human Vital Physiological Parameters Monitoring: A Wireless Body Area Technology Based Internet of Things PID Parameters Auto-Tuning on GPS-based Antenna Tracker Control using Fuzzy Logic Decision Support System for Thesis Graduation Recommendation Using AHP-TOPSIS Method Intelligent System of Parking Reservation and Monitoring on Campus using Internet of Things Concept Sistem Informasi Geografis Pariwisata Kota Semarang More cited articles
  1. A.I. Khan and S. Alhabsi, “Machine learning in computer vision,” Procedia Computer Science, vol. 167, pp. 1444-1452, 2020. doi: 10.1016/j.procs.2020.03.355
  2. T. Georgiou, Y.W. Chen, and M. Lew, “A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision,” International Journal of Multimedia Information Retrieval, vol. 9, pp. 135-170, 2020. doi: 10.1007/s13735-019-00183-w
  3. T. Lin, Y. Yang, J. Bayer, and H. Pfifister, “Sport-sxr immersive analytics in sports,” in CHI’20, Honolulu, USA, Apr. 17, 2020. pp. 1-7. doi: 10.1145/3334480.XXXXXXX
  4. G. Astolfi, F.P.C. Rezende, J.V.A. Porto, E.T. Matsubara, and H. Pistori, “Syntactic pattern recognition in computer vision,” ACM Computing Survey, vol. 54, no. 3, pp. 1-35, 2021. doi: 10.1145/3447241
  5. L. Manovich, “Computer vision, human senses, and language of the arts,” AI & Society, vol. 35, no. 4, pp. 1-8, 2020. doi: 10.1007/s00146-020-01094-9
  6. Y. Lu and S. Young, “A survey of public datasets for computer vision tasks in precision agriculture,” Computer and Electronics in Agriculture, vol. 178, no. 105760, pp. 1-13, 2020. doi: 10.1016/j.compag.2020.105760
  7. A. Morar, A. Moldoveanu, I. Mocanu, F. Moldoveanu, I.E. Radoi, V. Asavei, A. Gradinaru, and A. Butean, “A comprehensive survey of indoor localization methods based on computer vision,” Sensor, vol. 20, no. 2641, pp. 1-14, 2020. doi: 10.3390/s20092641
  8. D. Cazzato, M. Leo, C. Distante, and H. Voos, “When i look into your eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking,”, Sensor, vol. 20, no. 3739, pp. 1-42, 2020. doi: 10.3390/s20133739
  9. P. Wang, “Research on Sports Training Action Recognition based on Deep Learning,” Scientific Programming, vol. 21, pp. 1-8, 2021. doi: 10.1155/2021/3396878
  10. C. Zalluhoglu and N.I. Cinbis, “Collective Sports: A Multitask dataset for collective activity recognition,” Image and Vision Computing, vol. 94, no. 103870, pp. 1-11, 2020. doi: 10.1016/j.imavis.2020.103870
  11. Z. Pan and C. Li, “Robust basketball sports recognition by leveraging motion block estimation,” Signal Processing Image Communication, vol. 83, no. 115784, pp. 1-16, 2020. doi: 10.1016/j.image.2020.115784
  12. M.Y. Farhad, S. Hossain, M.D.R.K. Tanvir, and S.A. Chowdhury, “Sports-net18: Various Sports Classification using Transfer Learning,” in 2nd International Conference on Sustainable Technologies for Industry 4.0, Dhaka, Bangladesh, Dec. 19, 2020. pp. 1-4, 2020. doi: 10.1109/STI50764.2020.9350415
  13. L. Zhu, “Computer vision-driven evaluation system for assisted decision making in sports training,” Wireless Communications and Mobile Computing, vol. 2021, no. 1865538, pp. 1-7, 2021. doi: 10.1155/2021/1865538
  14. M.M. Baclig, N. Ergezinger, Q. Mei, M. Gul, S. Adeeb, and L. Westover, “A deep learning and computer vision-based multi-player tracker for squash,” Applied Science, vol. 10, no. 8793, pp. 1-16, 2020. doi: 10.3390/app10248793
  15. Q. Chen and M. Dong, “Detection and adaptive video processing hyperopia scene in sports video, Complexity,” vol. 2021, no. 6610760, pp. 1-13, 2021. doi: 10.1155/2021/6610760
  16. R. Zhang, L. Wu, Y. Yang, W. Wu, Y. Chen, and M. Xu, “Multicamera multiplayer tracking with deep player identification in sports video,” Pattern Recognition, vol. 102, no. 107260, pp. 1-12, 2020. doi: 10.1016/j.patcog.2020.107260
  17. B.S.Ramachandran, K. Santhanakrishnan, and M. Radhakrishnan, “Tracking of player in volleyball sports using a metaheuristic algorithm,” Journal of Physical Education and Sport, vol. 21, no. 3, pp. 1452-1460, 2021. doi: 10.7752/jpes.2021.03185
  18. C.B. Fernandez, G. Geiser, J. Krzyskowski, and K. Kipp, “Validity and reliability of computer vision-based smartphone apps for measuring barbell trajectory during the snatch,” Journal of Sports Sciences, vol. 38, no. 6, pp. 1-8, 2020. doi: 10.1080/02640414.2020.1729453
  19. Y. Li, H. He, and Z. Zhang, “Human motion quality assessment toward sophisticated sports scenes based on deeply-learned 3D CNN Model,” Journal Visual Communication and Image Representation, vol. 71, no. 102702, pp. 1-7, 2020. doi: 10.1016/j.jvcir.2019.102702
  20. H. Asadi, G. Zhou, J. Lee, V. Aggarwal, and D. Yu, “A computer vision approach for classifying isometric grip force exertion levels,” Ergonomics, vol. 63, no. 8, pp. 1-32, 2020. doi: 10.1080/00140139.2020.1745898
  21. W. Liu, “Beach sports image detection based on heterogeneous multi-processor and convolutional neural network,” Microprocessors and Microsystems, vol. 82, no. 103910, pp. 1-16, 2021. doi: 10.1016/j.micpro.2021.103910
  22. K. Joshi, V. Tripathi, C. Bose, and C. Bhardwaj, “Robust sports image classification using inception3 and neural network,” Procedia Computer Science, vol. 167, pp. 2374-2381, 2020. doi: 10.1016/j.procs.2020.03.290
  23. M. Ramesh and K. Mahesh, “A performance analysis of pre-trained neural network and design of CNN for sports classification,” in International Conference on Communication and Signal Processing, Chennai, India, Jul. 28, 2020, pp. 02103-0216. doi: 10.1109/ICCSP48568.2020.9182113
  24. J.A. Carlson, B. Liu, J.F. Sallis, J.A. Hipp, V.S. Staggs, J. Kerr, A. Papa, K. Dean, and N.M. Vasconcelos, “Automated high-frequency observations of physical activity using computer vision,” Official Journal of the American College of Sports Medicine, pp. 2029-2036, 2020. doi: 10.1249/MSS.0000000000002341
  25. L. Citraro, P.M. Neila, S. Savare, C. Dubou, F. Renaut, A. Hasfura, H.B. Shitrit, and P. Fua, “Real-time camera pose estimation for sports fields: machine vision and applications,” Machine Vision and Applications, vol. 31, no. 16, pp. 1-13, 2020. doi: 10.1007/s00138-020-01064-7
  26. D. Tang, “Hybridized hierarchical deep convolutional neural network for sports rehabilitation exercises,” IEEE Access, vol. 8, pp. 118969-118977, 2020. doi: 10.1109/ACCESS.2020.3005189
  27. X. Xu, L. Li, and A. Sharma, “Controlling messy errors in a virtual reconstruction of random sports image capture points for complex systems,” International Journal of System Assurance Engineering vol. 12, no. 3, pp. 1-8, 2021. doi: 10.1007/s13198-021-01094-y
  28. E.C. Latorre, M.D. Zuniga, E. Arriaza, F. Moya, and C. Nikulin, “Automatic registration of footsteps in contact regions for reactive agility training in sports,” Sensors, vol. 20, no. 1709, pp. 1-17, 2020. doi: 10.3390/s20061709
  29. J. Jung, S. Ha, W. Son, J. Jong, and H. Won, “Sport-light statistically principled crowdsourcing methods for sports highlight selection,” Journal of the Korean Statistical Safety, pp. 1-22, 2020. doi: 10.3390/s20061709
  30. D. Deng, J. Wu, J. Wang, Y. Wu, X. Xie, Z. Zhou, H. Zhang, X. Zhang, and Y. Wu, “Event-anchor: reducing human interactions in event annotation of racket sports videos,” in CHI’20, Yokohama, Japan, May 8, 2021, pp. 1-13, 2021. doi: 10.1145/3411764.3445431

Last update:

No citation recorded.

Last update: 2024-11-19 20:29:54

No citation recorded.