Segmentation of Football Video Broadcast

Authors

  • Sławomir Maćkowiak Faculty of Electronics and Telecommunications at Poznań University of Technology

Abstract

In this paper a novel segmentation system for football player detection in broadcasted video is presented. Proposed detection system is a complex solution incorporating a dominant color based segmentation technique of a football playfield, a 3D playfield modeling algorithm based on Hough transform and a dedicated algorithm for player tracking, player detection system based on the combination of Histogram of Oriented Gradients (HOG) descriptors with Principal Component Analysis (PCA) and linear Support Vector Machine (SVM) classification. For the shot classification the several classification technique SVM, artificial neural network and Linear Discriminant Analysis (LDA) are used. Evaluation of the system is carried out using HD (1280×720) resolution test material. Additionally, performance of the proposed system is tested with different lighting conditions (including non-uniform pith lightning and multiple player shadows) and various camera positions. Experimental results presented in this paper show that combination of these techniques seems to be a promising solution for locating and segmenting objects in a broadcasted video.

References

M. Muja and D. Lowe, “Fast Approximate Nearest Neighbors with Automatic Algorithm Classification,” in International Conference on Computer Vision Theory and Application (VISAPP 2009), Lizbona, 2009.

S. Hong, W. Yueshu, C. Wencheng, and Z. Jinxia, “Image Retrieval Based on MPEG-7 Dominant Color Descriptor,” in The 9th International Conference for Young Computer Scientists (ICYCS 2008), 2008, pp. 753 – 757.

L. Ying, L. Guizhong, and Q. Xueming, “Ball and Field Line Detection for Placed Kick Refinement,” in WRI Global Congress on Intelligent Systems (GCIS ’09), 2009, pp. 404–407, vol. 4.

R. Ren and J. M. Jose, “Football Video Segmentation Based on Video Production Strategy,” Lecture Notes in Computer Science, vol. 3408, pp. 433–446, 2005.

Q. Li, L. Zhang, J. You, D. Zhang, and P. Bhattacharya, “Dark line detection with line width extraction,” in International Conference on Image Processing (ICIP 2008), 2008, pp. 621–624.

X. Yu, H. C. Lai, S. X. F. Liu, and H. W. Leong, “A gridding Hough transform for detecting the straight lines in sports video,” in International Conference on Multimedia and Expo (ICME 2005), 2005, pp. 518–521.

T. T. Nguyen, X. D. Pham, and J. W. Jeon, “An improvement of the Standard Hough Transform to detect line segments,” in IEEE

International Conference on Industrial Technology (ICIT 2008), 2008, pp. 573–585.

G. Jiang, X. Ke, S. Du, and J. Chen, “A straight line detection based on randomized method,” in 9th International Conference on Signal Processing (ICSP 2008), 2008, pp. 1149–1152.

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 2005, pp. 886–893, vol. 1.

S. Maćkowiak, J. Konieczny, M. Kurc, and P. Ma´ckowiak, “A complex system for football player detection in broadcasted video,” in International Conference on Signals and Electronic Systems (ICSES 2010), 7–

September 2010, pp. 119–122.

S. Maćkowiak and J. Konieczny, “Player Extraction in Sports Video Sequences,” in International Conference on Systems, Signals and Image Processing (IWSSIP 2012), Vienna, Austria, 11–13 April 2012, pp. 423– 426.

D. Farin, S. Krabbe, P. de With, and W. Effelsberg, “Robust Camera Calibration for Sport Videos using Court Models,” Proceedings of SPIE, vol. 5307, pp. 80–91, 2004.

M. Grabner, H. Grabner, and H. Bischof, “Fast Aproximated SIFT,” in Asian Conference on Computer Vision, Washington, 1999.

D. Lowe, “Object recognition from local scale-invariant features,” in International Conference on Computer Vision, 2004.

S. Baker and I. Matthews, “Lucas-Kanade 20 years on: A unifying framework,” International Journal of Computer Vision, vol. 56, no. 3, pp. 221–255, 2004.

K. Nallaperumal, S. Ravi, C. N. K. Babu, R. K. Selvakumar, A. L. Fred, C. Seldev, and S. S. Vinsley, “Skin Detection Using Color

Pixel Classification with Application to Face Detection: A Comparative Study,” Proceedings of International Conference on Computational Intelligence and Multimedia Applications, 2007, vol. 3, pp. 436–441, 13-15 December 2007.

Z. Niu, X. Gao, D. Tao, and X. Li, “Semantic Video Shot Segmentation Based on Color Ratio Feature and SVM,” in 2008 International Conference on Cyberworlds, 22–24 September 2008, pp. 157–162.

N. Oezay and B. Sankur, “Automatic TV Logo Detection and Classification in Broadcast Videos,” in The 2009 European Signal Processing Conference (EUSIPCO 2009), Glasgow, Scotland, August 2009, pp. 839–843.sing Magazine, pp. 29–39, April 2012.

D. Donoho, “De-noising by soft tresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, May 1995.

C. Taswell, “The what, how, and why of wavelet shrinking denoising,” Stanford University, Tech. Rep. CT-1998-09, September 1998.

J. Szabatin, Fundamentals of Theory of Signals. Warszawa: WKiŁ, 1982, in polish.

Downloads

Published

2014-09-24

Issue

Section

ARCHIVES / BACK ISSUES