Algorithm for Real-Time Image Processing in the Robot Soccer

Abstract:

Vision system is an important part of the whole robot soccer system.In order to win the game, the robot system must be more quick and more accuracy.A color image segmentation method using improved seed-fill algorithm in YUV color space is introduced in this paper. The new method dramatically reduces the work of calculation,and speeds up the image processing. The result of comparing it with the old method based on RGB color space was showed in the paper.The second step of the vision sub system is identification the color block that separated by the first step.A improved seed fill algorithm is used in the paper.The implementation on MiroSot Soccer Robot System shows that the new method is fast and accurate.

• Periodical: Applied Mechanics and Materials (Volumes 143-144)
• Pages: 737-741
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.143-144.737
• Online since: December 2011
• Authors: Hai Bo Liu, Wei Wei Li, Yu Jie Dong
• Keywords: Image Segmentation, Real-Time Image Processing, Robot Soccer, Threshold

References

[1] James Bruce, Tucker Balch, Manuela Veloso. Fast and inexpensive color image segmentation for interactive robots[C], Intermation Conference on Intelligent Robots and System. Takamatsu. Japan, IEEE, 2000: 2061–(2066).

DOI: 10.1109/iros.2000.895274

[2] Maritinez-GomezLA, WeitzenfeldA. Real timevisionsystem for a small size league team[A], Proc. 1st IEEE-RAS Latin American Robotics Symposium[C]. Mexico City: ITAM, 2004: 343-349.

[3] Cho J Y, Jin S H, Pham X D, et al. A real-time object tracking system using a particle filter[C], RSJ, NJ, USA: IEEE, 2006: 2822-2827.

DOI: 10.1109/iros.2006.282066

[4] Vander Mark W, Vanden Heuvel J C. Stereo based obstacle detection with uncertainty in rough terrain[C]. Intelligent Vehicles Symposium. Piscataway, NJ, USA: IEEE, 2007: 1005-1012.

DOI: 10.1109/ivs.2007.4290248

[5] Grillo E, Matteucci M, Sorrenti G. D, Grtting the most from your color camera in a color-coded world[A]. D. Nardi et al. editors, RoboCup2004[C]. Springer, 2005: 221-235.

DOI: 10.1007/978-3-540-32256-6_18

[6] Yu J X, Cai Z X. Dead reckoning of mobile robot in complex terrain based on proprioceptive sensors[C]. International Conference on Machine Learning and Cybernetics. Piscataway, NJ, USA: IEEE , 2008: 1930-(1935).

DOI: 10.1109/icmlc.2008.4620722

[7] Jain R, Kasturi R, Schunck B G. Machine vision[M]. New York: McGraw Hill, (1995).