Abstract
This paper presents research work on the detection, tracking, and localization of the soccer ball in a broadcast soccer video and maps the ball locations to the global coordinate system of the soccer field. Because of the lack of reference points in these frames, the calculation of the global coordinates of the ball remains a very challenging task. This paper proposes to use an object-based algorithm and Kalman filter to detect and track the ball in such videos. Once the ball is located, frames are registered to static soccer field, and the absolute ball location is found in the field. The existing feature matching algorithms do not work well for frame registration, especially when involving lighting variations and large camera pan-tile-zoom change. To overcome this challenge, a new feature descriptor and matching algorithm that is robust to these deformations is developed and presented in this paper. Experimental results show the proposed algorithm is very effective and accurate.
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References
Yu, X., Tian, Q., Wan, K.W.: A novel ball detection framework for real soccer video. In: Proceedings of the International Conference on Multimedia and Expo, ICME 2003, vol.2, p. II-265-8 (2003)
Gong, Y., Sin, L.T., Chuan, C.H., Zhang, H., Sakauchi, M.: Automatic parsing of tv soccer programs. In: Proceedings of the International Conference on Multimedia Computing and Systems, pp. 167–174 (1995)
Kim, J.Y., Kim, T.Y.: Soccer ball tracking using dynamic Kalman filter with velocity control. In: Sixth International Conference on Computer Graphics, Imaging and Visualization, CGIV 2009, pp. 367–374 (2009)
Yu, X., Xu, C., Leong, H.W., Tian, Q., Tang, Q., Wan, K.W.: Trajectory-based ball detection and tracking with applications to semantic analysis of broadcast soccer video. In: Proceedings of the Eleventh ACM International Conference on Multimedia. MULTIMEDIA 2003, pp. 11–20. ACM, New York (2003)
D’Orazio, T., Ancona, N., Cicirelli, C., Nitti, M.: A ball detection algorithm for real soccer image sequences. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 1, pp. 210–213 (2002)
Liang, D., Liu, Y., Huang, Q., Gao, W.: A scheme for ball detection and tracking in broadcast soccer video. In: Ho, Y.-S., Kim, H.-J. (eds.) PCM 2005. LNCS, vol. 3767, pp. 864–875. Springer, Heidelberg (2005)
Ekin, A., Tekalp, A.: Robust dominant color region detection and color-based applications for sports video. In: Proceedings, International Conference on Image Processing, ICIP 2003, vol.1, p. I-21-4 (2003)
Ekin, A., Tekalp, A., Mehrotra, R.: Automatic soccer video analysis and summarization. IEEE Transactions on Image Processing 12, 796–807 (2003)
Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: BRIEF: Computing a Local Binary Descriptor Very Fast. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 1281–1298 (2012)
Leutenegger, S., Chli, M., Siegwart, R.: BRISK: Binary Robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (2011)
Canclini, A., Cesana, M., Redondi, A., Tagliasacchi, M., Ascenso, J., Cilla, R.: Evaluation of low-complexity visual feature detectors and descriptors. In: 18th International Conference on Digital Signal Processing (DSP), pp. 1–7 (2013)
Anderson, H.: Both lazy and efficient: compressed sensing and applications. Technical report, (Sandia National Laboratories), Report number: 2013-7521P (2013)
Desai, A., Lee, D.-J., Zhang, M.: Using accurate feature matching for unmanned aerial vehicle ground object tracking. In: Bebis, G., et al. (eds.) ISVC 2014, Part I. LNCS, vol. 8887, pp. 435–444. Springer, Heidelberg (2014)
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Desai, A., Lee, DJ., Wilson, C. (2014). Determine Absolute Soccer Ball Location in Broadcast Video Using SYBA Descriptor. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_57
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DOI: https://doi.org/10.1007/978-3-319-14364-4_57
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
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