Current - Issue
Year 2026 · Volume 5 · Issue 3
Original Article
Video-Based Handball Action Classification Using ResNet18-BiGRU with Temporal Attention
Prasad Raj Bingi1
Penugonda Ravi Kumar2
1 PG Scholar, Department of CSE, RGUKT – RK VALLEY, Vempalli, Andhra Pradesh, India. 2 Assistant Professor, Department of CSE, RGUKT – RK VALLEY, Vempalli, Andhra Pradesh, India.
Published Online: September-December 2026
Pages: 46-61
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503008
References
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2. M. Ivašić-Kos and M. Pobar, “Handball Action Dataset – UNIRI-HBD,” IEEE DataPort, 2021, doi: 10.21227/0g0a-fe06.
3. K. Host, M. Ivašić-Kos, and M. Pobar, “Action Recognition in Handball Scenes,” Lecture Notes in Networks and Systems, vol. 283, pp. 645–656, 2022, doi: 10.1007/978-3-030-80119-9_41.
4. K. Host and M. Ivašić-Kos, “An overview of Human Action Recognition in sports based on Computer Vision,” Heliyon, vol. 8, no. 6, p. e09633, 2022, doi: 10.1016/j.heliyon.2022.e09633.
5. K. Host, M. Pobar, and M. Ivašić-Kos, “Analysis of Movement and Activities of Handball Players Using Deep Neural Networks,” Journal of Imaging, vol. 9, no. 4, p. 80, 2023, doi: 10.3390/jimaging9040080.
6. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
7. K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1724–1734, doi: 10.3115/v1/D14-1179.
8. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.
9. D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” in International Conference on Learning Representations (ICLR), 2015.
10. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
11. S. Yan, Y. Xiong, and D. Lin, “Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018, pp. 7444–7452, doi: 10.1609/aaai.v32i1.12328.
12. Lea, M. D. Flynn, R. Vidal, A. Reiter, and G. D. Hager, “Temporal Convolutional Networks for Action Segmentation and Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1003–1012, doi: 10.1109/CVPR.2017.113.
13. V. Bazarevsky, I. Grishchenko, K. Raveendran, T. Zhu, F. Zhang, and M. Grundmann, “BlazePose: On-device Real-time Body Pose Tracking,” arXiv:2006.10204, 2020.
2. M. Ivašić-Kos and M. Pobar, “Handball Action Dataset – UNIRI-HBD,” IEEE DataPort, 2021, doi: 10.21227/0g0a-fe06.
3. K. Host, M. Ivašić-Kos, and M. Pobar, “Action Recognition in Handball Scenes,” Lecture Notes in Networks and Systems, vol. 283, pp. 645–656, 2022, doi: 10.1007/978-3-030-80119-9_41.
4. K. Host and M. Ivašić-Kos, “An overview of Human Action Recognition in sports based on Computer Vision,” Heliyon, vol. 8, no. 6, p. e09633, 2022, doi: 10.1016/j.heliyon.2022.e09633.
5. K. Host, M. Pobar, and M. Ivašić-Kos, “Analysis of Movement and Activities of Handball Players Using Deep Neural Networks,” Journal of Imaging, vol. 9, no. 4, p. 80, 2023, doi: 10.3390/jimaging9040080.
6. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
7. K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1724–1734, doi: 10.3115/v1/D14-1179.
8. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.
9. D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” in International Conference on Learning Representations (ICLR), 2015.
10. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
11. S. Yan, Y. Xiong, and D. Lin, “Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018, pp. 7444–7452, doi: 10.1609/aaai.v32i1.12328.
12. Lea, M. D. Flynn, R. Vidal, A. Reiter, and G. D. Hager, “Temporal Convolutional Networks for Action Segmentation and Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1003–1012, doi: 10.1109/CVPR.2017.113.
13. V. Bazarevsky, I. Grishchenko, K. Raveendran, T. Zhu, F. Zhang, and M. Grundmann, “BlazePose: On-device Real-time Body Pose Tracking,” arXiv:2006.10204, 2020.
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