Improving Ball Detection in Volleyball Using Deep Learning | ||
| Journal of AI and Data Mining | ||
| دوره 14، شماره 2، تیر 2026، صفحه 145-153 اصل مقاله (820.08 K) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2025.15807.2695 | ||
| نویسندگان | ||
| Mohammad Jadidi؛ Kourosh Kiani؛ Razieh Rastgoo* | ||
| Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. | ||
| چکیده | ||
| In recent years, the application of deep learning techniques has revolutionized various domains, including the realm of sports analytics. The analysis of ball tracking and trajectory in sports has become an increasingly vital area of research, driven by advancements in technology and the growing demand for data-driven insights in athletic performance. In volleyball, a sport characterized by rapid movements and strategic play, the ability to accurately track the trajectory of the ball is crucial for both training and competitive analysis. This paper proposes novel deep learning models for accurate volleyball ball detection and tracking. By incorporating attention mechanisms into the YOLOv8 and YOLOv10 architecture, our models significantly improve performance, particularly in challenging situations involving occlusions and fast movements. The proposed models across several metrics compared to baseline and other models. Specifically, achieved precision (94.2% and 94.7%, respectively) and recall (88.1% and 87.6%, respectively) and real-time processing speeds, making them suitable for various sports analytics applications. | ||
| کلیدواژهها | ||
| Volleyball detection؛ Deep learning؛ Convolutional Neural Network؛ Attention mechanism | ||
| مراجع | ||
|
[1] O. Elharrouss, N. Almaadeed, and S. Al-Maadeed. "A review of video surveillance systems," Journal of Visual Communication and Image Representation, vol. 77 103116, 2021.
[2] N. Robinson, B. Tidd, D. Campbell, D. Kulić, and P. Corke. "Robotic vision for human-robot interaction and collaboration: A survey and systematic review," ACM Transactions on Human-Robot Interaction, vol. 12, no. 1 pp. 1-66, 2023.
[3] K. Ranade, T. Khule, and R. More. "Object Recognition in Human Computer Interaction: A Comparative Analysis," arXiv:2411.04263, 2024.
[4] N. Esfandiari, K. Kiani, R. Rastgoo, “Development of a Persian Mobile Sales Chatbot based on LLMs and Transformer,” Journal of AI and Data Mining, vol. 12, no. 4, pp. 465-472, 2024.
[5] N. Esfandiari, K. Kiani, R. Rastgoo, “Transformer-based Generative Chatbot Using Reinforcement Learning,” Journal of AI and Data Mining, vol. 12, no. 3, pp. 349-358, 2024.
[6] A.M. Ahmadi, K. Kiani, R. Rastgoo, “A Transformer-based model for abnormal activity recognition in video,” Journal of Modeling in Engineering, vol. 22, no. 76, pp. 213-221, 2024.
[7] F. Bagherzadeh, R. Rastgoo, “Deepfake image detection using a deep hybrid convolutional neural network,” Journal of Modeling in Engineering, vol. 21, no. 75, pp. 19-28, 2023.
[8] M. Talebian, K. Kiani, R. Rastgoo, “A Deep Learning-based Model for Fingerprint Verification,” Journal of AI and Data Mining, vol. 12, no. 2, pp. 241-248, 2024.
[9] R. Rastgoo, K. Kiani, S. Escalera, “ZS-GR: zero-shot gesture recognition from RGB-D videos,” Multimedia Tools and Applications, vol. 82, no. 28, pp. 43781-43796, 2023.
[10] R. Rastgoo, K. Kiani, S. Escalera, “A deep co-attentive hand-based video question answering framework using multi-view skeleton,” Multimedia Tools and Applications, vol. 82, no. 1, pp. 1401-1429, 2023.
[11] H. Zaferani, K. Kiani, R. Rastgoo, “Real-time face verification on mobile devices using margin distillation,” Multimedia Tools and Applications, vol. 82, no. 28, pp. 44155-44173, 2023.
[12] S. Zarbafi, K. Kiani, R. Rastgoo, “Spoken Persian digits recognition using deep learning,” Journal of Modeling in Engineering, vol. 21, no. 74, pp. 163-172, 2023.
[13] N. Esfandiari, K. Kiani, R. Rastgoo, “A conditional generative chatbot using transformer model,” Neural Computing and Applications, 2025.
[14] R. Rastgoo, K. Kiani, “Face recognition using fine-tuning of Deep Convolutional Neural Network and transfer learning,” Journal of Modeling in Engineering, vol. 17, no. 58, pp. 103-111, 2019.
[15] F. Alinezhad, K. Kiani, R. Rastgoo, “A Deep Learning-based Model for Gender Recognition in Mobile Devices,” Journal of AI and Data Mining, vol. 11, no. 2, pp. 229-236, 2023.
[16] S. Shekarizadeh, R. Rastgoo, S. Al-Kuwari, M. Sabokrou, “Deep-disaster: unsupervised disaster detection and localization using visual data,” 26th International Conference on Pattern Recognition (ICPR), pp. 2814-2821, 2022.
[17] N. Majidi, K. Kiani, R. Rastgoo, “A deep model for super-resolution enhancement from a single image,” Journal of AI and Data Mining, vol. 8, no. 4, pp. 451-460, 2020.
[18] R. Rastgoo, V. Sattari-Naeini, “Gsomcr: Multi-constraint genetic-optimized qos-aware routing protocol for smart grids,” Iranian Journal of Science and Technology, Transactions of Electrical, Engineering, vol. 42, no. 2, pp. 185-194, 2018.
[19] R. Rastgoo, V. Sattari-Naeini, “Tuning parameters of the QoS-aware routing protocol for smart grids using genetic algorithm,” Applied Artificial Intelligence, vol. 30, no. 1, pp. 52-76, 2016.
[20] R. Rastgoo, V. Sattari Naeini, “A neurofuzzy QoS-aware routing protocol for smart grids,” 22nd Iranian Conference on Electrical Engineering (ICEE), pp. 1080-1084, 2014.
[21] K. Kiani, R. Rastgoo, A. Chaji, S. Escalera, “Image Inpainting Enhancement by Replacing the Original Mask with a Self-attended Region from the Input Image,” Journal of AI and Data Mining, vol. 13, no. 3, pp. 379-391, 2025.
[22] C.B. Santiago, A. Sousa, M.L. Estriga, L.P. Reis, and M. Lames, "Survey on team tracking techniques applied to sports." In 2010 International Conference on Autonomous and Intelligent Systems, AIS, pp. 1-6, 2010.
[23] P.R. Kamble, A.G. Keskar, and K.M. Bhurchandi, "Ball tracking in sports: a survey." Artificial Intelligence Review, vol. 52, pp.1655-1705, 2019.
[24] M. Takahashi, K. Ikeya, M. Kano, H. Ookubo, and T. Mishina, "Robust volleyball tracking system using multi-view cameras." In 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2740-2745, 2016.
[25] P. Kurowski, K. Szelag, W. Zaluski, and R. Sitnik, "Accurate ball tracking in volleyball actions to support referees." Opto-Electronics Review, vol. 26, no. 4, pp. 296-306, 2018.
[26] P.R. Kamble, A.G. Keskar, and K.M. Bhurchandi, "A deep learning ball tracking system in soccer videos," Opto-Electronics Review, vol. 27, no. 1, pp. 58-69, 2019.
[27] R. Beal, T.J. Norman, and S.D. Ramchurn. "Artificial intelligence for team sports: a survey," The Knowledge Engineering Review, vol. 34, e28, 2019.
[28] X. Yu, H.W. Leong, C. Xu, and Q. Tian. "Trajectory-based ball detection and tracking in broadcast soccer video," IEEE Transactions on multimedia, vol. 8, no. 6 pp. 1164-1178, 2006.
[29] N. Ishii, I. Kitahara, Y. Kameda, and Y. Ohta. "3D tracking of a soccer ball using two synchronized cameras," In Advances in Multimedia Information Processing, vol.8, pp. 196-205. 2007.
[30] K. Zhao, W. Jiang, X. Jin, and X. Xiao. "Artificial intelligence system based on the layout effect of both sides in volleyball matches," Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 3075-3084, 2021.
[31] D. Budden, S. Fenn, J. Walker, and A. Mendes. "A novel approach to ball detection for humanoid robot soccer," In AI 2012: Advances in Artificial Intelligence: 25th Australasian Joint Conference, vol. 25, pp. 827-838. 2012.
[32] B. Chakraborty and S. Meher. "A real-time trajectory-based ball detection-and-tracking framework for basketball video," Journal of Optics, vol. 42, pp.156-170, 2013.
[33] G. Huang, "An Effective Volleyball Trajectory Estimation and Analysis Method with Embedded Graph Convolution," International Journal of Distributed Systems and Technologies (IJDST), vol. 14, no. 2, pp.1-13, 2023.
[34] B. Chakraborty and S. Meher, "A trajectory-based ball detection and tracking system with applications to shot-type identification in volleyball videos," In 2012 International Conference on Signal Processing and Communications (SPCOM), pp. 1-5, 2012.
[35] N. Wojke, A. Bewley, and D. Paulus, "Simple online and real-time tracking with a deep association metric," In 2017 IEEE international conference on image processing (ICIP), pp. 3645-3649, 2017.
[36] X. Han, Q. Wang, and Y. Wang, "Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching," Applied Sciences, vol. 14, no. 4 pp. 1376, 2024.
[37] Y.J. Chen and Y.S. Wang, "Tracknetv3: Enhancing shuttlecock tracking with augmentations and trajectory rectification," In Proceedings of the 5th ACM International Conference on Multimedia in Asia, pp. 1-7. 2023.
[38] J.A. Vicente-Martínez, M. Márquez-Olivera, A. García-Aliaga, and V. Hernández-Herrera, "Adaptation of YOLOv7 and YOLOv7_tiny for soccer-ball multi-detection with DeepSORT for tracking by semi-supervised system," Sensors, vol. 23, no. 21, pp. 8693, 2023.
[39] V. Chao, H.Q. Nguyen, A. Jamsrandorj, Y.M. Oo, K.R. Mun, H. Park, S. Park, and J. Kim. "Tracking the Blur: Accurate Ball Trajectory Detection in Broadcast Sports Videos," In Proceedings of the 7th ACM International Workshop on Multimedia Content Analysis in Sports, pp. 41-49. 2024.
[40] I.K. Wibowo and N.A.H. Bernardt, "Prediction of Ball Position Using CNN Methods with Zed Camera on Goalkeeper Robot Application," IEEE Access, vol. 13, pp. 41559 – 41570, 2025.
[41] Ultralytics, YOLOv8, 2023. [online]. Available: https://docs.ultralytics.com/models/yolov8/
[42] A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, and G. Ding. "Yolov10: Real-time end-to-end object detection," arXiv:2405.14458, 2024.
[43] S. Woo, J. Park, J.Y. Lee, and I.S. Kweon, "Cbam: Convolutional block attention module," In Proceedings of the European conference on computer vision (ECCV), pp. 3-19, 2018.
[44] VolleyVision, 2024. [online]. Available: https://github.com/shukkkur/VolleyVision [Accessed: Sept. 25, 2023]. | ||
|
آمار تعداد مشاهده مقاله: 334 تعداد دریافت فایل اصل مقاله: 183 |
||