Abstract
Action recognition models and cumulative race time (CRT) are practical tools in sports analytics, providing insights into athlete performance, training, and strategy. Measuring CRT allows for identifying areas for improvement, such as specific sections of a racecourse or the effectiveness of different strategies. Human action recognition (HAR) algorithms can help to optimize performance, with machine learning and artificial intelligence providing real-time feedback to athletes. This paper presents a comparative study of HAR algorithms for CRT regression, examining two important factors: the frame rate and the regressor selection. Our results indicate that our proposal exhibits outstanding performance for short input footage, achieving a mean absolute error of 11 min when estimating CRT for runners that have been on the course for durations ranging from 8 to 20 h.
This work is partially funded by the the Spanish Ministry of Science and Innovation under project PID2021-122402OB-C22, and by the ACIISI-Gobierno de Canarias and European FEDER funds under project, ProID2021010012, ULPGC Facilities Net, and Grant EIS 2021 04.
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References
Carreira, J., Zisserman, A.: Quo Vadis, action recognition? A new model and the kinetics dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4724–4733 (2017)
Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6201–6210 (2018)
Feichtenhofer, C., Fan, H., Xiong, B., Girshick, R.B., He, K.: A large-scale study on unsupervised spatiotemporal representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3298–3308 (2021)
Freire-Obregón, D., Barra, P., Castrillón-Santana, M., de Marsico, M.: Inflated 3D ConvNet context analysis for violence detection. Mach. Vis. Appl. 33(15) (2022). https://doi.org/10.1007/s00138-021-01264-9
Freire-Obregón, D., Lorenzo-Navarro, J., Castrillón-Santana, M.: Decontextualized I3D ConvNet for ultra-distance runners performance analysis at a glance. In: International Conference on Image Analysis and Processing (ICIAP), pp. 242–253 (2022)
Freire-Obregón, D., Lorenzo-Navarro, J., Santana, O.J., Hernández-Sosa, D., Castrillón-Santana, M.: Towards cumulative race time regression in sports: I3D ConvNet transfer learning in ultra-distance running events. In: International Conference on Pattern Recognition (ICPR), pp. 805–811 (2022)
Freire-Obregón, D., Lorenzo-Navarro, J., Santana, O.J., Hernández-Sosa, D., Castrillón-Santana, M.: An X3D neural network analysis for runner’s performance assessment in a wild sporting environment. In: International Conference on Machine Vision Applications (MVA) (2023)
Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6546–6555 (2018)
Kay, W., et al.: The kinetics human action video dataset. CoRR (2017)
Nekoui, M., Cruz, F., Cheng, L.: Falcons: fast learner-grader for contorted poses in sports. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 3941–3949 (2020)
Nekoui, M., Cruz, F., Cheng, L.: EAGLE-Eye: extreme-pose action grader using detaiL bird’s-Eye view. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 394–402 (2021)
Pan, J., Gao, J., Zheng, W.: Action assessment by joint relation graphs. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6330–6339 (2019)
Parmar, P., Morris, B.T.: What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 304–313 (2019),
Penate-Sanchez, A., Freire-Obregón, D., Lorenzo-Melián, A., Lorenzo-Navarro, J., Castrillón-Santana, M.: TGC20ReId: a dataset for sport event re-identification in the wild. Pattern Recogn. Lett. 138, 355–361 (2020). https://doi.org/10.1016/j.patrec.2020.08.003
Pirsiavash, H., Vondrick, C., Torralba, A.: Assessing the quality of actions. In: European Conference on Computer Vision (2014)
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. ArXiv abs/1406.2199 (2014)
Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M.: C3D: generic features for video analysis. CoRR abs/1412.0767 (2014)
Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4489–4497 (2014)
Wang, X., Girshick, R.B., Gupta, A.K., He, K.: Non-local neural networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2017)
Yu, X., Rao, Y., Zhao, W., Lu, J., Zhou, J.: Group-aware contrastive regression for action quality assessment. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7899–7908 (2021)
Zhang, Y., et al.: Bytetrack: multi-object tracking by associating every detection box. In: European Conference on Computer Vision (2021)
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Freire-Obregón, D., Lorenzo-Navarro, J., Santana, O.J., Hernández-Sosa, D., Castrillón-Santana, M. (2023). A Large-scale Analysis of Athletes’ Cumulative Race Time in Running Events. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14233. Springer, Cham. https://doi.org/10.1007/978-3-031-43148-7_24
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