Visão Computacional no Esporte: Avaliação de Algoritmos Clássicos para Rastreamento de Bola no Voleibol
Resumo
O rastreamento visual de objetos é uma abordagem promissora para aplicações em análise esportiva. Neste trabalho, é avaliado o desempenho de algoritmos clássicos de rastreamento, como CSRT, KCF, MOSSE, MIL, TLD, MedianFlow e Boosting, na tarefa de rastrear a trajetória da bola de voleibol em vídeos. Para isso, foram utilizados registros de partidas profissionais e gravações em ambientes controlados. As métricas aplicadas incluem a IoU, FPS e MTE. Dentre os algoritmos, o CSRT apresenta o melhor desempenho em termos de precisão, enquanto MOSSE e KCF obtêm os resultados menos satisfatórios. Os achados deste estudo contribuem para o desenvolvimento de aplicações voltadas ao apoio da análise tática e técnica no contexto esportivo.Referências
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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.
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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. 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.
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.
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.
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.
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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.
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.
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. 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.
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.
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.
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.
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.
Publicado
28/05/2025
Como Citar
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.
Visão Computacional no Esporte: Avaliação de Algoritmos Clássicos para Rastreamento de Bola no Voleibol. In: ENCONTRO UNIFICADO DE COMPUTAÇÃO DO PIAUÍ (ENUCOMPI), 17. , 2025, Teresina/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 49-58.
DOI: https://doi.org/10.5753/enucompi.2025.9607.
