Abstract
The determination of the player’s gestures and actions in sports video is a key task in automating the analysis of the video material at a high level. In many sports views, the camera covers a large part of the sports arena, so that the resolution of player’s region is low. This makes the determination of the player’s gestures and actions a challenging task, especially if there is large camera motion. To overcome these problems, we propose a method based on curvature scale space templates of the player’s silhouette. The use of curvature scale space makes the method robust to noise and our method is robust to significant shape corruption of a part of player’s silhouette. We also propose a new recognition method which is robust to noisy sequences of data and needs only a small amount of training data.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Alon, J., Athitsos, V., Sclaroff, S.: Accurate and Efficient Gesture Spotting via Pruning and Subgesture Reasoning. In: Proc. the IEEE Workshop on Human-Computer Interaction, Beijing, China, October 2005, pp. 189–198 (2005)
Christmas, W.J., Kostin, A., Yan, F., Kolonias, I., Kittler, J.: A System for The Automatic Annotation of Tennis Matches. In: Fourth International Workshop on Content-based Multimedia Indexing, Riga (June 2005)
Corradini, A.: Dynamic Time Warping for Off-line Recognition of A Small Gesture Vocabulary. In: Proc. the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, Vancouver, Canada, pp. 82–89 (2001)
Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM 24, 381–395 (1981)
Kopf, S., Haenselmann, T., Effelsberg, W.: Shape-base Posture and Gesture Recognition in Videos. In: Electronic Imaging, San José, CA, January 2005, vol. 5682, pp. 114–124 (2005)
Lee, H.-K., Kim, J.H.: An HMM-Based Threshold Model Approach for Gesture Recognition. The IEEE Trans. on Pattern Analysis and Machine Intelligence 21(10), 961–973 (1999)
Mokhtarian, F., Bober, M.: Curvature Scale Space Representation: Theory, Applications & MPEG-7 Standardisation. Kluwer Academic, Dordrecht (2003)
Oka, R.: Spotting method for classification of real world data. The Computer Journal 41(8), 559–565 (1998)
Park, A.-Y., Lee, S.-W.: Gesture Spotting in Continuous Whole Body Action Sequences Using Discrete Hidden Markov Models. In: Gibet, S., Courty, N., Kamp, J.-F. (eds.) GW 2005. LNCS, vol. 3881, pp. 100–111. Springer, Heidelberg (2006)
Rabiner, L., Juang, B.-H.: Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs (1993)
Sullivan, J., Carlsson, S.: Recognizing and tracking human action. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 629–644. Springer, Heidelberg (2002)
Super, B.J.: Improving Object Recognition Accuracy and Speed through Non-Uniform Sampling. In: Proc. SPIE Conf. on Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision, Providence, RI, pp. 228–239 (2003)
Yan, F., Christmas, W., Kittler, J.: A Tennis Ball Tracking Algorithm for Automatic Annotation of Tennis Match. In: Proc. British Machine Vision Conference, Oxford, UK, September 2005, pp. 619–628 (2005)
Wang, J.R., Parameswaran, N.: Survey of Sports Video Analysis: Research Issues and Applications. In: Proc. Pan-Sydney Area Workshop on Visual Information Processing, Sydney, Australia, December 2004, vol. 36, pp. 87–90 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roh, MC., Christmas, B., Kittler, J., Lee, SW. (2006). Robust Player Gesture Spotting and Recognition in Low-Resolution Sports Video. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744085_27
Download citation
DOI: https://doi.org/10.1007/11744085_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33838-3
Online ISBN: 978-3-540-33839-0
eBook Packages: Computer ScienceComputer Science (R0)