[
World Athletics (2023), C1.1 & C2.1 Competition Rules & Technical Rules. Retrieved from https://www.worldathletics.org/about-iaaf/documents/book-of-rules (Accessed: 2023/09/04).
]Search in Google Scholar
[
Brooks, J. (2019). COCO Annotator. https://github.com/jsbroks/coco-annotator/.
]Search in Google Scholar
[
Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., & Sheikh, Y. A. (2019). Openpose: Realtime multi-person 2d pose estimation using part affinity fields. IEEE Transactions on Pattern Analysis and Machine Intelligence.
]Search in Google Scholar
[
Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T. S., & Zhang, L. (2020). HigherHRNet: Scale-aware representation learning for bottom-up human pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5386–5395).
]Search in Google Scholar
[
Contributors, M. (2020). Openmmlab pose estimation toolbox and benchmark. https://github.com/open-mmlab/mmpose.
]Search in Google Scholar
[
Díaz-Pereira, M. P., Gomez-Conde, I., Escalona, M., & Olivieri, D. N. (2014). Automatic recognition and scoring of olympic rhythmic gymnastic movements. Human movement science, 34, 63–80.
]Search in Google Scholar
[
Di Gironimo, G., Caporaso, T., Amodeo, G., Del Giudice, D. M., Lanzotti, A., & Odenwald, S. (2016). Outdoor tests for the validation of an inertial system able to detect illegal steps in race-walking. Procedia engineering, 147, 544–549.
]Search in Google Scholar
[
Gomez-Ezeiza, J., Torres-Unda, J., Tam, N., Irazusta, J., Granados, C., & Santos-Concejero, J. (2018). Race walking gait and its influence on race walking economy in world-class race walkers. Journal of sports sciences, 36 (19), 2235–2241.
]Search in Google Scholar
[
Hanley, B., Tucker, C. B., & Bissas, A. (2019). Assessment of iaaf raeewalk judges’ ability to detect legal and non-legal technique. Frontiers in sports and active living, 9.
]Search in Google Scholar
[
Hoga-Miura, K., Hirokawa, R., & Sugita, M. (2017). Reconstruction of walking motion without flight phase by using computer simulation on the world elite 20 km race walkers during official races. Slovak Journal of Sport Science, 2 (1), 59–75.
]Search in Google Scholar
[
Knicker, A., & Loch, M. (1990). Race walking technique and judging-the final report to the international athletic foundation research project. New Studies in Athletics, 5 (3), 25–38.
]Search in Google Scholar
[
Lee, J. B., Mellifont, R. B., Burkett, B. J., & James, D. A. (2013). Detection of illegal race walking: a tool to assist coaching and judging. Sensors, 13 (12), 16065–16074.
]Search in Google Scholar
[
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., . . . Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740–755).
]Search in Google Scholar
[
Ludwig, K., Scherer, S., Einfalt, M., & Lienhart, R. (2021). Self-supervised learning for human pose estimation in sports. In 2021 ieee international conference on multimedia & expo workshops (icmew) (pp. 1 6).
]Search in Google Scholar
[
Menting, S. G. P., Hanley, B., Elferink-Gemser, M. T., & Hettinga, F. J. (2022). Pacing behaviour of middle-long distance running & race-walking athletes at the iaaf u18 and u20 world championship finals. European Journal of Sport Science, 22 (6), 780–789.
]Search in Google Scholar
[
Pavei, G., & La Torre, A. (2016). The effects of speed and performance level on race walking kinematics. Sport Sciences for Health, 12 (1), 35–47.
]Search in Google Scholar
[
Santoso, D. R., & Setyanto, T. A. (2013). Development of precession instrumentation system for differentiate walking from running in race walking by using piezoelectric sensor. Sensors & Transducers, 155 (8), 120.
]Search in Google Scholar
[
Suzuki, T., Takeda, K., & Fujii, K. (2022). Automatic fault detection in race walking from a smartphone camera via fine-tuning pose estimation. In 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE) (pp. 631-632).
]Search in Google Scholar
[
Taborri, J., Palermo, E., & Rossi, S. (2019). Automatic detection of faults in race walking: A comparative analysis of machine-learning algorithms fed with inertial sensor data. Sensors, 19 (6), 1461.
]Search in Google Scholar
[
Uchida, I., Scott, A., Shishido, H., & Kameda, Y. (2021). Automated offside detection by spatio-temporal analysis of football videos. In Proceedings of the 4th international workshop on multimedia content analysis in sports (pp. 17–24).
]Search in Google Scholar
[
Vapnik, V. (1999). The nature of statistical learning theory. In (p. 138). Springer science & business media.
]Search in Google Scholar
[
Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., & Girshick, R. (2019). Detectron2. https://github.com/facebookresearch/detectron2.
]Search in Google Scholar
[
Xu, C., Fu, Y., Zhang, B., Chen, Z., Jiang, Y.-G., & Xue, X. (2019). Learning to score figure skating sport videos. IEEE transactions on circuits and systems for video technology, 30 (12), 4578–4590.
]Search in Google Scholar