Abstract
Multiple players tracking plays a key role in volleyball analysis. Due to the demand of developing effective tactics for professional events, players’ 3D information like speed and trajectory is needed. Although, 3D information can solve the occlusion relation problem, complete occlusion and similar feature between players may still reduce the accuracy of tracking. Thus, this paper proposes a motion vector and players’ features based particle filter for multiple players tracking in 3D space. For the prediction part, a motion vector prediction model combined with Gaussian window model is proposed to predict player’s position after occlusion. For the likelihood estimation part, a 3D distance likelihood model is proposed to avoid error tracking between two players. Also, a number detection likelihood model is used to distinguish players. With the proposed multiple players tracking algorithm, not only occlusion relation problem can be solved, but also physical features of players in the real world can be obtained. Experiment which executed on an official volleyball match video (Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo Metropolitan Gymnasium) shows that our tracking algorithm can achieve 91.9 % and 92.6 % success rate in the first and third set.
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References
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Dong, X.M., Yuan, K.: A robust Cam Shift tracking algorithm based on multi-cues fusion. In: 2nd International Conference on Advanced Computer Control (ICACC), vol. 1, pp. 521–524 (2010)
Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Fluids Eng. 82(1), 35–45 (1960)
Ndiour, I.J., Vela, P.A.: A local extended Kalman filter for visual tracking. In: 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)
Cheng, X., Shiina, Y., Zhuang, X., Ikenaga, T.: Player tracking using prediction after intersection based particle filter for volleyball match video. In: Asia-Pacific Signal and Information Processing Association, Annual Summit and Conference (APSIPA ACS) (2014)
Shiina, Y., Ikenaga, T.: Dual model particle filter for irregular moving object tracking in sports scenes. In: Asia-Pacific Signal and Information Processing Association, Annual Summit and Conference (APSIPA ASC) (2011)
Lee, S., Horio, K.: Human tracking with particle filter based on locally adaptive appearance model. J. Sig. Process. 18(4), 229–232 (2014)
Morais, E., Goldenstein, S., Ferreira, A., Rocha, A.: Automatic tracking of indoor soccer players using videos from multiple cameras. In: 25th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 174–181 (2012)
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2003)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: International Conference on Image Processing, Proceedings, vol. 1, pp. I-900–I-903 (2002)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)
Acknowledgement
This work was supported by KAKENHI (26280016) and Waseda University Grant for Special Research Projects (2015K-222).
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Zhuang, X., Cheng, X., Huang, S., Honda, M., Ikenaga, T. (2015). Motion Vector and Players’ Features Based Particle Filter for Volleyball Players Tracking in 3D Space. 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_38
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DOI: https://doi.org/10.1007/978-3-319-24075-6_38
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