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Publisher: Elsevier
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputingMilieux_PERSONALCOMPUTING
Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] E. Kijak, G. Gravier, P. Gros, L. Oisel, F. Bimbot, HMM Based Structuring of Tennis Videos Using Visual and Audio Cues, in: IEEE Internatinal Conference on Multimedia and Expo, vol. 3, 309{312, 2003.
    • [2] I. Kolonias, W. Christmas, J. Kittler, Tracking the Evolution of a Tennis Match Using Hidden Markov Models, in: Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, vol. 3138, 1078{1086, 2004.
    • [3] Y. Huang, C. Chiou, F. Sandnes, An Intelligent Strategy for the Automatic Detection of Highlights in Tennis Video Recordings, Expert Systems with Applications 36 (2009) 9907{9918.
    • [4] The Hawkeye Tennis System, http://www.hawkeyeinnovations.co.uk, 2014.
    • [5] X. Yu, C. Sim, J. R. Wang, L. Cheong, A Trajectory-based Ball Detection and Tracking Algorithm in Broadcast Tennis Video, in: ICIP, vol. 2, 1049{1052, 2004.
    • [6] B. Ekinci, M. Gokmen, A Ball Tracking System for O ine Tennis Videos, in: International Conference on Visualization, Imaging and Simulation, 2008.
    • [7] G. Zhu, C. Xu, Q. Huang, W. Gao, Action Recognition in Broadcast Tennis Video, in: International Conference on Pattern Recognition, 2006.
    • [8] F. Yan, J. Kittler, K. Mikolajczyk, D. Windridge, Automatic Annotation of Court Games with Structured Output Learning, in: International Conference on Pattern Recognition, 2012.
    • [9] W. Christmas, A. Kostin, F. Yan, I. Kolonias, J. Kittler, A System for the Automatic Annotation of Tennis Matches, in: Fourth International Workshop on Content-Based Multimedia Indexing, 2005.
    • [10] F. Yan, W. Christams, J. Kittler, Layered Data Association Using Graph-Theoretic Formulation with Application to Tennis Ball Tracking in Monocular Sequences, PAMI 30(10) (2008) 1814{1830.
    • [11] E. Kijak, G. Gravier, L. Oisel, P. Gros, Audiovisual Integration for Tennis Broadcast Structuring, in: International Workshop on ContentBased Multimedia Indexing, 2003.
    • [19] T. Joachims, T. Finley, C. Yu, Cutting-Plane Training of Structural SVMs, Machine Learning 77 (2009) 27{59.
    • [21] B. Taskar, C. Guestrin, D. Koller, Max-Margin Markov Networks, in: NIPS, 2003.
    • [22] A. Ng, M. Jordan, On Discriminative vs. Generative Classi ers: A Comparison of Logistic Regression and Naive Bayes, in: NIPS, 2002.
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