Application and Analysis of Visual Tracking Algorithms for Ball Detection in Volleyball Matches
Abstract
The use of Computer Vision has revolutionized sports analysis. In the context of volleyball, ball tracking presents challenges due to its size, speed, and occlusions. Given this, this study aims to evaluate tracking algorithms, including CSRT, MIL, TLD, MedianFlow, and Boosting, as well as train a YOLO model for ball detection. The analysis was conducted based on metrics such as IoU, FPS, and Mean Tracking Error. The methodology involved testing on videos of professional matches and controlled environments. The results indicate that controlled conditions favor superior performance, providing support for the development of systems to assist refereeing and sports analysis.
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Zhihui, S. and Sijing, C. (2021). Offline method-based online tracking method and apparatus, computer device, and storage medium.
Babenko, B., Yang, M.-H., and Belongie, S. (2010). Robust object tracking with online multiple instance learning. IEEE transactions on pattern analysis and machine intelligence, 33(8):1619–1632.
Bashar, M., Islam, S., Hussain, K. K., Hasan, M. B., Rahman, A., and Kabir, M. H. (2022). Multiple object tracking in recent times: A literature review. arXiv preprint arXiv:2209.04796.
Bolme, D. S., Beveridge, J. R., Draper, B. A., and Lui, Y. M. (2010). Visual object tracking using adaptive correlation filters. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2544–2550.
Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.
Brdjanin, A., Dardagan, N., Dzigal, D., and Akagic, A. (2020). Single object trackers in opencv: A benchmark. In 2020 International Conference on Innovations in Intelligent Systems and Applications (INISTA), pages 1–6, Novi Sad, Serbia. IEEE.
Chen, F., Wang, X., Zhao, Y., Lv, S., and Niu, X. (2022). Visual object tracking: A survey. Computer Vision and Image Understanding, 222:103508.
Freund, Y., Schapire, R. E., et al. (1996). Experiments with a new boosting algorithm. In icml, volume 96, pages 148–156. Citeseer.
Fu, Y., Qin, P., Zhang, J., and Lu, Z. (2024). Joint ai inference and target tracking at network edge: A hybrid offline-online design for uav-enabled network. IEEE Transactions on Wireless Communications.
Ghosh, I., Ramasamy Ramamurthy, S., Chakma, A., and Roy, N. (2023). Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(5):e1496.
Gudauskas, J. and Matusevičius, Ž. (2021). Multiple object tracking for video-based sports analysis. In CEUR workshop proceedings: IVUS 2021: Information society and university studies 2021: Proceedings of the 26th international conference on information society and university studies (IVUS 2021), volume 2915, pages 1–10, Kaunas, Lithuania. CEUR-WS.
Han, X., Wang, Q., and Wang, Y. (2024). Ball tracking based on multiscale feature enhancement and cooperative trajectory matching. Applied Sciences, 14(4):1376.
Henriques, J. F., Caseiro, R., Martins, P., and Batista, J. (2014). High-speed tracking with kernelized correlation filters. IEEE transactions on pattern analysis and machine intelligence, 37(3):583–596.
Huang, G. (2023). An effective volleyball trajectory estimation and analysis method with embedded graph convolution. International Journal of Distributed Systems and Technologies (IJDST), 14(2):1–13.
Javed, S., Danelljan, M., Khan, F. S., Khan, M. H., Felsberg, M., and Matas, J. (2022). Visual object tracking with discriminative filters and siamese networks: a survey and outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):6552–6574.
Jocher, G., Chaurasia, A., and Qiu, J. (2023). Ultralytics yolov8.
Kalal, Z., Mikolajczyk, K., and Matas, J. (2010). Forward-backward error: Automatic detection of tracking failures. In 2010 20th International Conference on Pattern Recognition, pages 2756–2759, Istanbul, Turkey. IEEE.
Kalal, Z., Mikolajczyk, K., and Matas, J. (2011). Tracking-learning-detection. IEEE transactions on pattern analysis and machine intelligence, 34(7):1409–1422.
Liu, S., Liu, D., Srivastava, G., Połap, D., and Woźniak, M. (2020). Overview of correlation filter based algorithms in object tracking. complex intell syst.
Liu, X. and Caesar, H. (2023). Offline tracking with object permanence. arXiv.org, abs/2310.01288.
Lukezic, A., Vojir, T., Cehovin Zajc, L., Matas, J., and Kristan, M. (2017). Discriminative correlation filter with channel and spatial reliability. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6309–6318, Honolulu, Hawaii, USA. IEEE.
Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., and Kim, T.-K. (2021). Multiple object tracking: A literature review. Artificial intelligence, 293:103448.
Naik, B. T., Hashmi, M. F., and Bokde, N. D. (2022). A comprehensive review of computer vision in sports: Open issues, future trends and research directions. Applied Sciences, 12(9):4429.
Nunes, V. d. S. (2023). Tópicos em visão computacional: uma revisão sistemática com aplicações em economia 4.0.
Silva, R. B. d. (2021). Desenvolvimento de tecnologia de informação e comunicação (tic) para análise instantânea de desempenho de voleibol.
Volleybal Playoffs (2024). Canal do YouTube: VolleybalPlayoffs. [link]. Acesso em: 9 nov. 2024.
Wu, Y., Lim, J., and Yang, M.-H. (2013). Online object tracking: A benchmark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2411–2418, Portland, Oregon, USA. IEEE, IEEE.
Yardimci, O. and Şimşek Tekerek, A. (2022). Performance comparison of online and offline tracking algorithms. In Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, volume 12096, pages 1209604–1209604, Orlando, Florida, USA. SPIE.
Zhihui, S. and Sijing, C. (2021). Offline method-based online tracking method and apparatus, computer device, and storage medium.
Published
2025-07-20
How to Cite
BATISTA, Leandro Natálio F.; SOUSA, Carlos Estevão B.; SANTANA, Cleidson S. de; MARIA, Carlos Eduardo S. de; SANTOS, Felipe G. dos; MORAIS, Renê Douglas N. de.
Application and Analysis of Visual Tracking Algorithms for Ball Detection in Volleyball Matches. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 52. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 109-120.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2025.7500.
