Abstract
Computer vision technologies are widely used in sports to control the quality of training. However, there are only a few approaches to recognizing the punches of a person engaged in boxing training. All existing approaches have used manual feature selection and trained on insufficient datasets. We introduce a new approach for recognizing actions in an untrimmed video based on three stages: removing frames without actions, action localization and action classification. Furthermore, we collected a sufficient dataset that contains five classes in total represented by more than 1000 punches in total. On each stage, we compared existing approaches and found the optimal model that allowed us to recognize actions in untrimmed videos with an accuracy 87%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Beijing Skyline Interaction Technology Co, The Fastest Fist. https://store.steampowered.com/app/544540/The_Fastest_Fist/
Kasiri, S., Fookes, C., Sridharan, S., Morgan, S.: Fine-grained action recognition of boxing punches from depth imagery. Comput. Vis. Image Underst. 159, 143–153 (2017). https://doi.org/10.1016/j.cviu.2017.04.007
Kasiri, S., Fookes, C., Sridharan, S., Morgan, S., Martin, T.: Combat sports analytics: boxing punch classification using overhead depth imagery, pp. 4545–4549. IEEE (2015)
Feichtenhofer, C., Fan, H., Malik, J., He, K.: SlowFast networks for video recognition (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R, Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: CVPR (2014)
Simonyan, S., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NIPS (2014)
Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition (2016)
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks (2014)
Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset (2017)
Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: Quo vadis, action recognition? A new model and the kinetics dataset (2017)
Lin, T., Zhao, X., Su, H., Wang, C., Yang, M.: BSN: boundary sensitive network for temporal action proposal generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 3–21. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_1
Huber, P.: Robust estimation of a location parameter. Ann. Stat. 53, 73–101 (1964). https://doi.org/10.1214/aoms/1177703732
McClish, D.K.: Analyzing a portion of the ROC curve. Med. Decis. Making 9, 190–195 (1989). https://doi.org/10.1177/0272989X8900900307
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Broilovskiy, A., Makarov, I. (2021). Human Action Recognition for Boxing Training Simulator. In: van der Aalst, W.M.P., et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-72610-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72609-6
Online ISBN: 978-3-030-72610-2
eBook Packages: Computer ScienceComputer Science (R0)