Abstract
In this paper we propose a novel approach to build a single shot regressor, called SFLNet, that directly predicts a parameter set relating a sports field seen in an input frame to its metric model. This problem is challenging due to the huge intra-class variance of sports fields and the large number of free parameters to be predicted. To address these issues, we propose to train our regressor in combination with semantic segmentation in a multi-task learning framework. We also introduce an additional module to exploit the spacial consistency of sports fields, which boosts both regression and segmentation performances. SFLNet can be learned with a training dataset that can be semi-automatically built from human annotated point-to-point correspondences. To our knowledge, this work is the first attempt to solve this sports field localization problem relying only on an end-to-end deep learning framework. Experiments on our new dataset based on basketball games validate our approach over baseline methods.
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Notes
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We avoid sampling when the game is stopping in order not to sample duplicate frames.
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References
Theagarajan, R., Pala, F., Zhang, X., Bhanu, B.: Soccer: who has the ball? Generating visual analytics and player statistics. In: CVPR Workshop (2018)
Giancola, S., Amine, M., Dghaily, T., Ghanem, B.: SoccerNet: a scalable dataset for action spotting in soccer videos. In: CVPR Workshop (2018)
Gupta, A., Little, J.J., Woodham, R.J.: Using Line and Ellipse Features for Rectification of Broadcast Hockey Video, In: CRV (2011)
Chen, J., Zhu, F., Little, J.J.: A two-point method for PTZ camera calibration in sports. In: WACV (2018)
Homayounfar, N., Fidler, S., Urtasun, R.: Sports field localization via deep structured models. In: CVPR (2017)
Chen, J., Little, J.J.: Sports camera calibration via synthetic data. arXiv preprint (2018)
Sharma, R.A., Bhat, B., Gandhi, V., Jawahar, C.V.: Automated top view registration of broadcast football videos. In: WACV (2018)
Zhang, H., et al.: Context encoding for semantic segmentation. In: CVPR (2018)
Wu, H., Zhang, J., Huang, K.: FastFCN: rethinking dilated convolution in the backbone for semantic segmentation. arXiv preprint (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the CVPR (2016)
Lin, C.-H., Lucey, S.: Inverse compositional spatial transformer networks. In: CVPR (2017)
Girshick, R.: Fast R-CNN. In: ICCV (2015)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)
Kendall, A., Cipolla, R.: Modelling uncertainty in deep learning for camera relocalization. In: ICRA (2016)
Kendall, A., Cipolla, R.: Geometric loss functions for camera pose regression with deep learning. In: CVPR (2017)
Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DOF camera relocalization. In: ICCV (2015)
Brahmbhatt, B., Gu, J., Kim, K., Hays, J., Kautz, J.: Geometry-aware learning of maps for camera localization. In: CVPR (2018)
Sattler, T., Zhou, Q., Pollefeys, M., Leal-Taixé, L.: Understanding the limitations of CNN-based absolute camera pose regression. In: CVPR (2019)
Carr, P., Sheikh, Y., Matthews, I.: Point-less calibration: camera parameters from gradient-based alignment to edge images. In: WACV (2012)
Kim, H., Hong, K.S.: Soccer video mosaicing using self-calibration and line tracking. In: ICPR (200)
Yamada, A., Shirai, Y., Miura, J.: Tracking players and a ball in video image sequence and estimating camera parameters for 3D interpretation of soccer games. In: ICPR (2002)
Farin, D., Krabbe, S., With, P.H.N., de Effelsberg, W.: Robust camera calibration for sport videos using court models. In: Electronic Imaging (2004)
Watanabe, T., Haseyama, M., Kitajima, H.: A soccer field tracking method with wire frame model from TV images. In: ICIP (2004)
Wang, F., Sun, L., Yang, B., Yang, S.: Fast arc detection algorithm for play field registration in soccer video mining. In: ICSMC (2006)
Okuma, K., Little, J.J., Lowe, D.G.: Automatic rectification of long image sequences. In: ACCV (2004)
Dubrofsky, E., Woodham, R.J.: Combining line and point correspondences for homography estimation. In: Bebis, G., et al. (eds.) ISVC 2008. LNCS, vol. 5359, pp. 202–213. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89646-3_20
Hess, R., Fern, A.: Improved video registration using nondistinctive local image features. In: CVPR (2007)
Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint (2017)
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: CVPR (2017)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2004)
Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)
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Tarashima, S. (2020). SFLNet: Direct Sports Field Localization via CNN-Based Regression. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_48
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