Abstract
3D position tracking of the ball plays a crucial role in professional volleyball analysis. In volleyball games, the constraint conditions that limit the performance of the ball tracking include the fast irregular movement of the ball, the small-size of the ball, the complex background as well as the occlusion problem caused by players. This paper proposes a ball size adaptive (BSA) tracking window, a ball feature likelihood model and an anti-occlusion likelihood measurement (AOLM) base on Particle Filter for improving the accuracy. By adaptively changing the tracking windows according to the ball size, it is possible to track the ball with changing size in different video images. On the other hand, the ball feature likelihood enables to track stably even in complex background. Furthermore, AOLM based on a multiple-camera system solves the occlusion problems since it can eliminate the low likelihood caused by occlusion. Experimental results which are based on the HDTV video sequences (2014 Inter High School Games of Men’s Volleyball) captured by four cameras located at the corners of the court show that the success rate of the ball’s 3D position tracking achieves 93.39 %.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chen, H.T., Tsai, W.J., Lee, S.Y., Yu, J.Y.: Ball tracking and 3D trajectory approximation with applications to tactics analysis from single-camera volleyball sequences. Multimedia Tools Appl. 60(3), 641–667 (2012)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Dong, X.M., Yuan, K.: A robust CamShift tracking algorithm based on multi-cues fusion. In: 2nd International Conference on Advanced Computer Control (ICACC), vol. 1, pp. 521–524 (2010)
Lowe, D.G.: Distinctive image features from scale invariant key points. J. Comput. Vis. 60, 91–110 (2004)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 85, 35–45 (1960)
Ndiour, I.J., Vela, P.A.: A local extended Kalman filter for visual tracking. In: 49th IEEE Conference on Decision and Control (CDC), pp. 2498–2504 (2010)
Kitagawa, G.: Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graph. Stat. 5(1), 1–25 (1996)
Hess, R., Fern, A.: Discriminatively trained particle filter for complex multi-object tracking. In: CVPR 2009, pp. 240–247 (2009)
Huang, T.S., Llach, J., Zhang, C.: A method of small object detection and tracking based on particle filters. In: 19th IEEE International Conference on Pattern Recognition, ICPR (2008)
Guo, C., Lu, Y., Ikenaga, T.: Robust online tracking using orientation and color incorporated adaptive models in particle filter. In: 4th International Conference on New Trends in Information Science and Service Science, pp. 281–286 (2010)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)
Campbell, C., Ying, Y.: Learning with support vector machines. Synth. Lect. Artif. Intell. Mach. Learn. 5(1), 1–95 (2011)
Acknowledgment
This work was supported by KAKENHI (26280016) and Waseda University Grant for Special Research Projects (2015 K-222).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Cheng, X., Zhuang, X., Wang, Y., Honda, M., Ikenaga, T. (2015). Particle Filter with Ball Size Adaptive Tracking Window and Ball Feature Likelihood Model for Ball’s 3D Position Tracking in Volleyball Analysis. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-24075-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24074-9
Online ISBN: 978-3-319-24075-6
eBook Packages: Computer ScienceComputer Science (R0)