Tomohiro Shimizu
ABSTRACT
In this paper, we propose a method to predict the future shot direction in a tennis match using pose information and player position. As far as we know, there is no work that deals with such a predictive task, so there is no shot direction dataset as yet. Therefore, using a YouTube tennis match video, we construct an time of impact and shot direction dataset. To reduce annotation costs, we propose a method to automatically label the shot direction. Moreover, we propose a method to predict the future shot direction using the constructed dataset. The shot direction is predicted using LSTM(long short-time memory), from sequential pose information up to the time of impact and the player position. We employ OpenPose to extract the position of skeleton joints. In the experiment, we evaluate the accuracy of shot direction prediction and verify the effectiveness of the proposed method. Since there are no studies that predict future shot direction, we set four baseline methods to evaluate the effectiveness of our proposed method.
- YI Abdel-Aziz, HM Karara, and Michael Hauck. 2015. Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. Photogrammetric Engineering & Remote Sensing , Vol. 81, 2 (2015), 103--107.Google Scholar
Cross Ref
- Andrew Blake and Michael Isard. 1997. The condensation algorithm-conditional density propagation and applications to visual tracking. In Advances in Neural Information Processing Systems. 361--367.Google Scholar
- Jiaxin Cai and Xin Tang. [n. d.]. RGB video based tennis action recognition using a deep weighted long short-term memory. arXiv preprint arXiv:1808.00845 ([n. d.]).Google Scholar
- Zixi Cai, Helmut Neher, Kanav Vats, David A Clausi, and John Zelek. 2019. Temporal hockey action recognition via pose and optical flows. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (5th International Workshop on Computer Vision in Sports (CVsports)). 0--0.Google ScholarCross Ref
- Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2017. Realtime multi-person 2d pose estimation using part affinity fields. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). 1302--1310.Google ScholarCross Ref
- Lluis Castrejon, Kaustav Kundu, Raquel Urtasun, and Sanja Fidler. 2017. Annotating object instances with a polygon-rnn. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). 5230--5238.Google ScholarCross Ref
- Teofilo De Campos, Mark Barnard, Krystian Mikolajczyk, Josef Kittler, Fei Yan, William Christmas, and David Windridge. 2011. An evaluation of bags-of-words and spatio-temporal shapes for action recognition. In IEEE Workshop on Applications of Computer Vision (WACV2011). 344--351.Google ScholarDigital Library
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009). 248--255.Google ScholarCross Ref
- Hossein Fani, Amin Mirlohi, Hawre Hosseini, and Rainer Herperst. 2018. Swim stroke analytic: Front crawl pulling pose classification. In 2018 25th IEEE International Conference on Image Processing (ICIP). 4068--4072.Google ScholarCross Ref
- Sofia Gourgari, Georgios Goudelis, Konstantinos Karpouzis, and Stefanos Kollias. 2013. Thetis: Three dimensional tennis shots a human action dataset. In IEEE Conference on Computer Vision and Pattern Recognition Workshops. 676--681.Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation , Vol. 9, 8 (1997), 1735--1780.Google Scholar
- Diederik P. Kingma and Jimmy Ba. [n. d.]. Adam: A method for stochastic optimization. CoRR , Vol. abs/1412.6980 ([n. d.]).Google Scholar
- Ryunosuke Kurose, Masaki Hayashi, Takeo Ishii, and Yoshimitsu Aoki. 2018. Player pose analysis in tennis video based on pose estimation. In 2018 International Workshop on Advanced Image Technology (IWAIT 2018). 1--4.Google ScholarCross Ref
- Johannes Landlinger, Stefan Lindinger, Thomas Stöggl, Herbert Wagner, and Erich Müller. 2010. Key factors and timing patterns in the tennis forehand of different skill levels. Journal of sports science & medicine , Vol. 9, 4 (2010), 643.Google Scholar
- Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In European conference on computer vision (ECCV2014). 740--755.Google ScholarCross Ref
- Tomoyuki Mishina Masaki Takahashi, Toshiyuki Nakamura. 2014. A method of soccer ball tracking from several viewpoints using a machine learning algorithm. In The Institute of Image Information and Television Engineers. 5--8.Google Scholar
- Vito Reno, Nicola Mosca, Roberto Marani, Massimiliano Nitti, Tiziana D'Orazio, and Ettore Stella. 2018. Convolutional neural networks-based ball detection in tennis games. In IEEE Conference on Computer Vision and Pattern Recognition Workshops. 1758--1764.Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. [n. d.]. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ([n. d.]).Google Scholar
- Michal Sipko and William Knottenbelt. 2015. Machine learning for the prediction of professional tennis matches. MEng computing-final year project, Imperial College London (2015).Google Scholar
- Jialei Wang, Peilin Zhao, and Steven CH Hoi. [n. d.]. Exact soft confidence-weighted learning. arXiv preprint arXiv:1206.4612 ([n. d.]).Google Scholar
- Fei Yan, W Christmas, and Josef Kittler. 2005. A tennis ball tracking algorithm for automatic annotation of tennis match. In British machine vision conference (BMVC 2005), Vol. 2. 619--628.Google ScholarCross Ref
- Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Wang Ling. [n. d.]. Learning to compose words into sentences with reinforcement learning. arXiv preprint arXiv:1611.09100 ([n. d.]).Google Scholar
- Xiangzeng Zhou, Qiang Huang, Lei Xie, and Stephen Cox. 2013. A two layered data association approach for ball tracking. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) . 2317--2321.Google ScholarCross Ref
Index Terms
- Prediction of Future Shot Direction using Pose and Position of Tennis Player
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