Object Tracking Method based on 3D Cartoon Animation in Broadcast Soccer Videos

doi:10.23940/ijpe.18.08.p14.17741784

Abstract

Abstract:

In this paper, a system of broadcasting football video conversion into 3D cartoon animation is designed. When a sports event is broadcasted, multiple cameras are usually deployed around the field. However, at the same time, only one camera’s video is available to viewers. Viewers hope to be able to watch the game from other viewpoints. Moreover, after a major sports game, some web portals provide cartoon animations of goal events. However, this is time-consuming and tedious, and only a single viewpoint is provided. Based on the proposed object tracking methods, this paper employs computer vision and computer graphics techniques to design a system that can generate 3D cartoon animations of soccer games. This allows users to watch the game from different viewpoints.

Submitted on May 9, 2018; Revised on June 30, 2018; Accepted on July 30, 2018
References: 16

Chunlong Xie, Zhiqian Zhang, Chunsheng Wang, and Zhengqing Liu. Object Tracking Method based on 3D Cartoon Animation in Broadcast Soccer Videos [J]. Int J Performability Eng, 2018, 14(8): 1774-1784.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 0

	D. Qin, “Study on the Algorithm for Automatic Classification of Video Content and Multi Feature Combination based on SVM,” Shanghai Jiao Tong University, 2009
	S. Yang, “Research on Digital Media and Pan Animation,” Yunnan Normal University, 2014
	Y. Lu, “ Research on Video Shot Detection and Classification based on Content,” Shandong Normal University, 2010
	R. R. Wang, “Generation of 3D Character Animation based on Motion Database,” Graduate University of Chinese Academy of Sciences, 2006
	J. Tan and L. D. Wu, “Video Summarization Method based on Feature Animation,” Computer Application, Vol. 10, pp. 3960-3962, 2011
	K. Matsui, M. Iwase, M. Agata, T. Tanaka, and N. Ohnishi, “Soccer Image Sequence Computed by a Virtual Camera,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 860-865, 1998
	T. Bebie and H. Bieri, “Soccer Man-Reconstructing Soccer Games from Video Sequences,” in Proceedings of IEEE International Conference on Image Processing, Vol. 1, pp. 898-902, 1998
	X. Yu, X. Yan, T. S. Hay, and H. W. Leong, “3D Reconstruction and Enrichment of Broadcast Soccer Video,” in Proceedings of ACM International Conference on Multimedia, pp. 260-263, 2014
	T. Kanade and P. J. Narayanan, “Virtualized Reality: Perspectives on 4D Digitization of Dynamic Events,” IEEE Computer Graphics and Applications, Vol. 27, No. 3, pp. 32-40, 2012
	Humanoid Animation Working Group, “Specification for a Standard Humanoid Version 1.1,” (http://h-anim.org/Specifications/H-Anim1.1/)
	G. Zhu, C. Xu, Q. Huang, and W. Gao, “Automatic Multi-Player Detection and Tracking in Broadcast Sports Video using Support Vector Machine and Particle Filter,” in Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1629 -1632, 2013
	S. Jiang, Q. Ye, W. Gao, and T. Huang, “A New Method to Segment Playfield and its Applications in Match Analysis in Sports Video,” in Proceedings of ACM International Conference on Multimedia, pp. 292-295, 2014
	R. Hartly and A. Zisserman, “Multiple View Geometry in Computer Vision,” 2nd Edition, Cambridge University Press, 2013
	Z. Zhang, “A Flexible New Technique for Camera Calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 11, pp. 1330-1334, 2012
	P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, “Color-based Probabilistic Tracking,” in Proceedings of European Conference on Computer Vision, pp. 661-675, 2012
	V. Vapnik, “The Nature of Statistical Learning Theory,” Springer-Verlag, New York, 1995