Abstract
This paper proposes a novel framework for tennis player detection and tracking. The algorithm is built on (1) a powerful court-line pixel detection method utilizing intensity and texture pattern, (2) a fast RANSAC-based line parameter estimation which also determines line extents, and (3) a player segmentation and tracking algorithm exploiting knowledge of tennis court model. The content of the video is then explored at a highly semantic level. The framework was tested extensively on numerous challenging video sequences with various court environments and lighting conditions. The results show the robustness and the promising direction of our algorithm.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Extremely Fast Line Algorithm, http://www.edepot.com/linee.html
Roger Federer Highlights Video, http://www.rogerfedererpoints.com
Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 174–188 (2002)
Avidan, S.: Ensemble Tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA (2005)
Calvo, C., Micarelli, A., Sangineto, E.: Automatic Annotation of Tennis Video Sequences. In: Proceedings of the 24th DAGM Symposium on Pattern Recognition, London, UK, pp. 540–547 (2002)
Clarke, J., Carlsson, S., Zisserman, A.: Detecting and tracking linear features efficiently. In: Proc 7th British Machine Vision Conference, Edinburgh, pp. 415–424 (1996)
Collins, R., Liu, Y., Leordeanu, M.: Online Selection of Discriminative Tracking Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1631–1643 (2005)
Farin, D., Han, J., de With, P.H.N.: Multi-level analysis of sports video sequences. In: SPIE Conference on Multimedia Content Analysis, Management, and Retrieval, San Jose, USA, pp. 1–12 (2006)
Farin, D., Han, J., de With, P.H.N., Lao, W.: Automatic Tracking Method for Sports Video Analysis. In: 26th Symposium on Information Theory, Brussels, Belgium, pp. 309–316 (2005)
Farin, D., Krabbe, S., de With, P.H.N., Effelsberg, W.: Robust Camera Calibration for Sport Videos Using Court Models. In: SPIE Storage and Retrieval Methods and Applications for Multimedia, pp. 80–91 (2004)
Jiang, Y.C., Lai, K.T., Hsieh, C.H., Lai, M.F.: Player Detection and Tracking in Broadcast Tennis Video. In: Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology, Tokyo, Japan, pp. 759–770 (2009)
Segarra, D.L.: Tennis Stroke Detection in Video Sequences. In: Master Degree Project, Stockholm, Sweden (2005)
Sudhir, G., Lee, C., Jain, K.: Automatic Classification of Tennis Video for High-level Content-based Retrieval. In: Proceedings of International Workshop on Content-Based Access of Image and Video Databases, Washington DC, USA, pp. 81–90 (1998)
Wang, J.R., Parameswaran, N.: Survey of sports video analysis research issues and applications. In: Workshop on Visual Information Processing, Sydney, Australia, pp. 87–90 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dang, B., Tran, A., Dinh, T., Dinh, T. (2010). A Real Time Player Tracking System for Broadcast Tennis Video. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds) Intelligent Information and Database Systems. ACIIDS 2010. Lecture Notes in Computer Science(), vol 5991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12101-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-12101-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12100-5
Online ISBN: 978-3-642-12101-2
eBook Packages: Computer ScienceComputer Science (R0)