INTELLIGENT COMPUTER VISION SYSTEM FOR SCORE DETECTION IN BASKETBALL

Ivan Ćirić, Maša Milošević, Nikola Ivačko, Aleksandra Cvetković, Milan Pavlović, Dušan Krstić

DOI Number
https://doi.org/10.22190/FUACR230822006C
First page
075
Last page
085

Abstract


Development of an intelligent computer vision system for Smart IoT basketball training and entertainment includes the development of a range of various subsystems, where score detection subsystem is playing a crucial role. This paper proposes the architecture of such a score detection subsystem to improve reliability and accuracy of the RFID technology used primarily for verification purposes. Challenges encompass both hardware-software interdependencies, optimal camera selection, and cost-effectiveness considerations. Leveraging machine learning algorithms, the vision-based subsystem aims not only to detect scores but also to facilitate online video streaming. Although the use of multiple cameras offers expanded field coverage and heightened precision, it concurrently introduces technical intricacies and increased costs due to image fusion and escalated processing requirements. This research navigates the intricate balance between achieving precise score detection and pragmatic system development. Through precise camera configuration optimization, the proposed system harmonizes hardware and software components.

Keywords

Computer vision, intelligent score detection, basketball training system, convolutional neural networks

Full Text:

PDF

References


N. Quijano, C. Ocampo-Martinez, J. Barreiro-Gomez, G. Obando, A. Pantoja and E. Mojica-Nava, "The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory," IEEE Control Systems Magazine, vol. 37, no. 1, pp. 70-97, Feb. 2017, DOI: 10.1109/MCS.2016.2621479.

H. B. Shitrit, J. Berclaz, F. Fleuret, and P. Fua, "Tracking Multiple People under Global Appearance Constraints," IEEE International Conference on Computer Vision, pp. 137-144, Nov. 2011, DOI: 10.1109/ICCV.2011.6126235.

V. Ramanathan, J. Huang, S. Abu-El-Haija, A. Gorban, K. Murphy, and F. Li, "Detecting events and key actors in multi-person videos," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2016, https://doi.org/10.48550/arXiv.1511.02917

D. Acuna, "Towards real-time detection and tracking of basketball players using deep neural networks," 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 2017

B. Chakraborty and S. Meher, "A real-time trajectory-based ball detection-and-tracking framework for basketball video," Journal of optics, vol. 42, no. 2, pp. 156-170, 2013, DOI: 10.1007/s12596-012-0108-7.

W. Pengyu and G. Wanna, "Image Detection and Basketball Training Performance Simulation Based on Improved Machine Learning," Journal of Intelligent and Fuzzy Systems, vol. 40, no. 2, DOI:10.3233/JIFS-189243

M. Hu, Q. Hu, "Design of basketball game image acquisition and processing system based on machine vision and image processor," Microprocessors and Microsystems, vol. 82, no. 1, 2021, DOI: 10.1016/j.micpro.2021.103904.

H. Li and M. Zhang, "Artificial Intelligence and Neural Network-Based Shooting Accuracy Prediction Analysis in Basketball," Mobile Information Systems, vol. 2021, 2021, https://doi.org/10.1155/2021/4485589.

X. Fu, S. Yue, D. Pan, "Camera-based Basketball Scoring Detection Using Convolutional Neural Network," International Journal of Automation and Computing vol. 18, pp. 266–276, 2021, https://doi.org/10.1007/s11633-020-1259-7.

C. Anthony B. Petilla, G. Daniel G. Yap, N. Y. Zheng, J. Ilao, "Single Player Tracking in Multiple Sports Videos," Mechatronics and Machine Vision in Practice, April 2018, DOI: 10.1007/978-3-319-76947-9_6.

X. Fu, K. Zhang, C. Wang, C. Fan, "Multiple player tracking in basketball court videos," Journal of Real-Time Image Processing, vol. 17, no. 3, DOI: 10.1007/s11554-020-00968-x.

S. P. K. Santhosh and B. Kaarthick, "An Automated Player Detection and Tracking in Basketball Game," CMC-Comput. Mater. Contin vol. 58, no. 3, pp. 625-639, 2019, DOI:10.32604/cmc.2019.05161.

L. Wu, Z. Yang, J. He, M. Jian, Y. Xu, D. Xu, C. W. Chen, "Ontology based global and collective motion patterns for event classification in basketball videos," IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 7, pp. 2178-2190, July 2020, DOI: 10.1109/TCSVT.2019.2912529.

L. Wu, Z. Yang, Q. Wang, M. Jian, B. Zhao, J. Yan, C. W. Chen, "Fusing motion patterns and key visual information for semantic event recognition in basketball videos," Neurocomputing, vol. 413, pp. 217-229, 2020, https://doi.org/10.1016/j.neucom.2020.07.003.

L. Liu, "Objects detection toward complicated high remote basketball sports by leveraging deep CNN architecture," Future Generation Computer Systems, vol. 119, pp. 31-36, 2021, https://doi.org/10.1016/j.future.2021.01.020

Q. Huang, W. Gao, H. Yao et al., "Event tactic analysis based on broadcast sports video," IEEE Transactions on Multimedia, vol. 11, no. 1, pp. 49–67, 2009, DOI: 10.1109/TMM.2008.2008918.

T. S. Fu, H. T. Chen, C. L. Chou, W. J. Tsai, "Screen-strategy analysis in broadcast basketball video using player tracking," IEEE Conference on Visual Communications and Image Processing, Tainan, Taiwan, 2011, pp. 1-4, DOI: 10.1109/VCIP.2011.6115927.

C. Tian, V. De Silva, M. Caine, S. Swanson, "Use of Machine Learning to Automate the Identification of Basketball Strategies Using Whole Team Player Tracking Data," Applied Sciences, vol. 10, no. 1, 2020, https://doi.org/10.3390/app10010024

Y. Yoon, H. Hwang, Y. Choi, M. Joo, H. Oh, I. Park, K. H., "Analyzing basketball movements and pass relationships using realtime object tracking techniques based on deep learning," IEEE Access, vol. 7, pp. 56564-56576, 2019, doi: 10.1109/ACCESS.2019.2913953.

Y. Zhao, R. Yang, G. Chevalier, R. C. Shah, R. Romijnders, "Applying deep bidirectional LSTM and mixture density network for basketball trajectory prediction," Optik, vol. 158, pp. 266-272, 2018, https://doi.org/10.1016/j.ijleo.2017.12.038.

Y. Uchida, N. Mizuguchi, M. Honda, K. Kanosue, "Prediction of shot success for basketball free throws: Visual search strategy," European Journal of Sport Science, vol. 14, no. 5, pp. 426–432, 2014, DOI: 10.1080/17461391.2013.866166

P. Zuccolotto, M. Manisera, M. Sandri, "Big data analytics for modeling scoring probability in basketball: The effect of shooting under high-pressure conditions," International Journal of Sports Science & Coaching, vol. 13, no. 4, pp. 569–589, 2018, https://doi.org/10.1177/1747954117737492.




DOI: https://doi.org/10.22190/FUACR230822006C

Refbacks

  • There are currently no refbacks.


Print ISSN: 1820-6417
Online ISSN: 1820-6425