One Image Segmentation and Discrimination Method of Live Video System Applied in Sport Game

Author(s): Han Laiguo* .

Journal Name: Recent Advances in Electrical & Electronic Engineering

Volume 12 , Issue 3 , 2019

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Abstract:

Background: In the field of video image processing, the segmentation and tracking method has become a hot research field. According to the characteristics of soccer video and VAR (Video Assistant Referee) System, in the paper, it proposes a fast algorithm of segmentation and tracking of the players in the video.

Methods: First, according to complementary advantages on the color expression in RGB (Red, Green, Blue) space and HSI (Hue, Saturation, Intensity) space, it adopts the method based on combining the main and auxiliary space to make segmentation of the objects in the video. Then, it compares with the normalized color histogram of targets to make identification of which team the players belong to.

Results: Finally, it adopts the context features of the players in the soccer video sequence; it combines the method of template matching to realize the player tracking in the soccer video.

Conclusion: The experimental results show that the tracking algorithm proposed in this paper can solve the objects occlusion problem of the different players in the soccer match, and can track the different players steadily, and it also can be adopted in the VAR system.

Keywords: Color space, video tracking correlation, template matching and segmentation, VAR, histogram, confidence level.

[1]
T. Aach, "Andre Kaup and R. Mester, “Statistical model-based change detection in moving video", Signal Processing, vol. 31, no. 2, pp. 165-180, 1993.
[2]
G. Adiv, "Determining three-dimensional motion and structure form optical flow generated by several moving objects", IEEE Trans. Pattern Anal. Mach. Intell., vol. 7, no. 4, pp. 384-401, 1985.
[3]
J. Wang, and E. Adelson, "Representing moving images with layers", IEEE Trans. Image Process., vol. 3, no. 5, pp. 625-6381994, .
[4]
Y. Deng, and B. Manjunath, "Unsupervised Segmentation of colortexture regions in images and video", IEEE Trans. Patt. Anal. Mach. Intell.. Vol. 23, no.8, pp. 800-8102001.
[5]
R.P. Schumaker, O.K. Solieman, and H. Chen, Predictive modeling for sports and gaming., Spring, US, 2010.
[6]
L.Y. Duan, M. Xu, Q. Tian, and X. Changsheng, "A unified framework for semantic shot classification in sports video", IEEE Trans. Multimed., vol. 7, no. 6, pp. 1066-1083, 2005.
[7]
N. Babaguchi, Y. Kawai, and T. Kitahashi, "Event based indexing of broadcasted sports video by intermodal collaboration", IEEE Trans. Multimed., vol. 4, no. 1, pp. 68-75, 2002.
[8]
M. Everingham, and A. Zisserman, Automated person identification in video/Image and Video Retrieval.Springer Berlin Heidelberg, . pp. 289-298, 2004.
[9]
M.C. Hu, M.H. Chang, J.L. Wu, and L. Chi, "Robust camera calibration and player tracking in broadcast basketball video", IEEE Trans. Multimed., vol. 13, no. 2, pp. 266-279, 2011.
[10]
G. Zhu, C. Xu, Q. Huang, Y. Rui, S. Jiang, W. Gao, and H. Yao, "Event tactic analysis based on broadcast sports video", IEEE Trans. Multimed., vol. 1, no. 1, pp. 49-67, 2009.
[11]
H. Li, J. Tang, S. Wu, Y. Zhang, and S. Lin, "Automatic detection and analysis of player action in moving background sports video sequences", IEEE Trans. Circ. Syst. Video Tech., vol. 20, no. 3, pp. 351-364, 2010.


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Article Details

VOLUME: 12
ISSUE: 3
Year: 2019
Page: [270 - 276]
Pages: 7
DOI: 10.2174/2352096511666180605081412
Price: $58

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