Nikos Katsarakis
ABSTRACT
As pervasive computing technologies are gradually penetrating sport, we are witnessing a proliferating number of research systems that can track athletes during training and/or in competition. Indeed, athlete tracking is a particularly challenging research tasks, which is also a key enabler for a wide range of applications such as ambient personalized broadcasting. In this paper we survey person tracking systems for sport applications and illustrate their limitations for realistic sports environments. As a real-life example we present the robust and high-performance person tracking system developed at the Athens Information Technology, and explain its inability to deal with occlusions, interlacing, adverse and changing light conditions and mostly the strain of the athletes, which are very common in high activity athletic scenes. We also present techniques for dealing with these challenges, along with an architecture for building ambient camera selection environments for broadcasting purposes. These techniques form the basis for the person tracking and ambient camera selection systems that are developed in the scope of the my-e-Director 2012 EC project, which is working towards a realistic prototype system for the London 2012 Olympics.
- BBC research, the automated coverage project. http://www.bbc.co.uk/rd/projects/virtual/automatedcoverage/index.shtml.Google Scholar
- S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2):174--188, February 2002. Google ScholarDigital Library
- S. Azodolmolky, N. Dimakis, V. Mylonakis, G. Souretis, J. Soldatos, A. Pnevmatikakis, and L. Polymenakos. Middleware for In-door Ambient Intelligence: The PolyOmaton System. In 4th IFIP TC-6 Networking Conference, 2nd Next Generation Networking Workshop(NGNM), Waterloo, Canada, May 2005.Google Scholar
- S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems. Artech House radar library, 1999.Google Scholar
- G. Bradski, A. Kaehler, and V. Pisarevsky. Learning-based computer vision with intel's open source computer vision library, 2005.Google ScholarCross Ref
- R. Brunelli, A. Brutti, P. Chippendale, O. Lanz, M. Omologo, P. Svaizer, and F. Tobia. A generative approach to audio-visual person tracking. In Multimodal Technologies for Perception of Humans, Proceedings of the First International CLEAR Evaluation Workshop, pages 55--68, Southampton, UK, 2007. Springer LNCS 4122. Google ScholarDigital Library
- E. H. Chi, G. Borriello, G. Hunt, and N. Davies. Guest editors' introduction: Pervasive computing in sports technologies. IEEE Pervasive Computing, 4(3):22--25, 2005. Google ScholarDigital Library
- W. Du, J.-B. Hayet, J. Piater, and J. Verly. Collaborative multi-camera tracking of athletes in team sports. In Workshop on Computer Vision Based Analysis in Sport Environments (CVBASE), pages 2--13, Graz, Austria, May 2006.Google Scholar
- J. Hayet, T. Mathes, J. Czyz, J. Piater, J. Verly, and B. Macq. A modular multi-camera framework for team sports tracking. In Advanced Video and Signal Based Surveillance (AVSS 2005), pages 493--498, Como, Italy, September 2005.Google ScholarCross Ref
- R. E. Kalman. A new approach to linear filtering and prediction problems. Transactions of the ASME--Journal of Basic Engineering, 82(Series D):35--45, 1960.Google Scholar
- C. Needham and R. Boyle. Tracking multiple sports players through occlusion, congestion and scale, September 2001.Google Scholar
- K. Okuma, A. Taleghani, N. de Freitas, J. J. Little, and D. G. Lowe. A boosted particle filter: Multitarget detection and tracking. In T. Pajdla and J. Matas, editors, 8th European Conference on Computer Vision (ECCV 2004), volume 3021 of Lecture Notes in Computer Science, pages 28--39. Springer, 2004.Google Scholar
- P. Perez, J. Vermaak, and A. Blake. Data fusion for visual tracking with particles. Proceedings of IEEE, 92(3):495--513, 2004.Google ScholarCross Ref
- G. S. Pingali, Y. Jean, and I. Carlbom. Real-time tracking for enhanced tennis broadcasts. In Computer Vision and Pattern Recognition (CVPR '98), pages 260--265, Santa Barbara, CA, USA, June 1998. Google ScholarDigital Library
- A. Pnevmatikakis and L. Polymenakos. Robust estimation of background for fixed cameras. In 15th International Conference on Computing (CIC '06), pages 37--42, Mexico City, Mexico, November 2006. IEEE Computer Society. Google ScholarDigital Library
- J. Skaloud, Q. Ladetto, B. Merminod, M. Vetterli, M. Gyr, A. Marcacci, P. Luthi, and Y. Schutz. Athletic Analysis With a Racing Heart. GPS World, pages 18--22, 2001.Google Scholar
- A. W. B. Smith and B. C. Lovell. Visual tracking for sports applications. In APRS Workshop on Digital Image Computing Pattern Recognition and Imaging for Medical Applications, pages 79--84, Brisbane, Australia, February 2005.Google Scholar
- C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell., 22(8):747--757, 2000. Google ScholarDigital Library
- A. Stergiou, A. Pnevmatikakis, and L. Polymenakos. The ait outdoor tracker for vehicles and pedestrians in CLEAR 2007. In CLEAR'07 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships, Baltimore, May 2007.Google Scholar
- R. Urtasun, D. Fleet, and P. Fua. Monocular 3d tracking of the golf swing. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2:932--938, June 2005. Google ScholarDigital Library
- P. A. Viola and M. J. Jones. Rapid object detection using a boosted cascade of simple features. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), pages 511--518, Kauai, HI, USA, December 2001.Google ScholarCross Ref
- M. Xu, J. Orwell, L. Lowey, and D. Thirde. Architecture and algorithms for tracking football players with multiple cameras. IEE Proceedings on Vision, Image and Signal Processing, 152:232--241, April 2005.Google ScholarCross Ref
Index Terms
- Person tracking for ambient camera selection in complex sports environments
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