Abstract
This paper presents a robust player tracking method for sports video analysis. In order to track agile player stably and robustly, we employ multiple models method, with a mean shift procedure corresponding to each model for player localization. Furthermore, we define pseudo measurement via fusing the measurements obtained by mean shift procedure. And the fusing coefficients are built from two likelihood functions: one is image based likelihood; the other is motion based association probability. Experimental results show effectiveness of our method in the hard case of player tracking literature.
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References
Bar-Shalom, Y., Fortmann, T.: Tracking and Data Association. Academic Press, London (1988)
http://vismod.media.mit.edu/vismod/demos/football/tracking.htm
Colins, R., Lipton, A., Fujiyoshi, H., Kanade, T.: Algorithms for cooperative multisensor surveillance. Proceedings of the IEEE 89(10), 1456–1477 (2001)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003)
Mazor, E., Averbuch, A., Bar-Shalom, Y., Dayan, J.: Interacting multiple model methods in target tracking: a survey. IEEE Transactions on Aerospace and Electronic Systems 34(1), 103–123 (1998)
Kyung-Nam, K., Ramakrishna, R.S.: Vision-based eye-gaze tracking for human computer interface. In: IEEE SMC 1999 Conference Proceedings, vol. 2, pp. 324–329 (1999)
Bar-Shalom, Y., Li, X.R.: Estimation and applications to tracking and navigation. Academic Press Inc., London (1995)
Seo, Y., Choi, S.H., Kim, H.W., Hong, K.S.: Where are the ball and players? Soccer game analysis with color-based tracking and image mosaic. In: Proceedings of International Conference on Image Analysis and Processing, pp. 196–203 (1997)
Pers, J., Kovacic, S.: Tracking People in Sport: Making Use of Partially Controlled Environment. In: Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns, pp. 374–382 (2001)
Pers, J., Kovacic, S.: Computer Vision System for Tracking Players in Sports Games. In: Proceedings of the First Intenatioal Workshop on Image and Signal Processing and Analysis, pp. 81–86 (2000)
Intille, S., Bobick, A.: Closed-world tracking. In: Proceedings of Intenatioal Conference on Computer Vision, pp. 672–678 (1995)
Okuma, K., Taleghani, A., Freitas, N., Little, J., Lowe, D.: A Boosted Particle filter: Multitarget detection and tracking. In: Proceedings of European Conference on Computer Vision 2004, pp. 28–39 (2004)
Misu, T., Gohshi, S., Izumi, Y., Fujita, Y., Naemura, M.: Robust Tracking of Athletes Using Multiple Features of Multiple Views. Journal of WSCG 12(1-3) (2004)
Comaniciu, D., Ramesh, V.: Mean Shift and Optimal Prediction for Efficient Object Tracking. In: Proceedings of the IEEE Intenational Conference on Image Processing, pp. 70–73 (2000)
Kirubarajan, T., Bar-Shalom, Y., Blair, W.D., Watson, G.A.: IMMPDAF for Radar Management and Tracking Benchmark with ECM. IEEE Transaction on Aerospace and Electronic Systems 34(4), 1115–1134 (1998)
Nickls, K., Hutchinson, S.: Estimating Uncertainty in SSD-Based Feature Tracking. Image and Vision Computing 20, 47–58 (2002)
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhong, X., Zheng, N., Xue, J. (2006). Pseudo Measurement Based Multiple Model Approach for Robust Player Tracking. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_78
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DOI: https://doi.org/10.1007/11612704_78
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