A novel color detection method based on HSL color space for robotic soccer competition

In a robotic soccer competition, the locations of the ball and the robot are recognized through the vision system in a strong dynamic environment; therefore, the robot and the computer need to identify the object in real-time and accurately through the vision system. Due to the influence of the game ball under different brightnesses, the color clusters of glossy materials will produce a nonlinear behavior and will cause difficulty of recognition. Thus, we propose a novel color detection method based on hue, saturation, and lightness (HSL) color space in this paper. Firstly, by utilizing the hue histogram, the hue and saturation (HS) plane and the hue and lightness (HL) plane can be obtained respectively. In addition, based on the obtained information from two planes, the databases can be built for decreasing the running-time and increasing the recognition success rate of identifying object. Moreover, a novel algorithm is presented for the game ball in the robotic soccer competition. Finally, some examples under different brightnesses are illustrated to demonstrate the preciseness and the accuracy of the proposed method are better than the traditional method.

[1]  Peng Shengze,et al.  Research based on the HSV humanoid robot soccer image processing , 2010, 2010 Second International Conference on Communication Systems, Networks and Applications.

[2]  Huang Yumin,et al.  A PHYSICAL APPROACH TO COLOR IMAGE UNDERSTANDING , 1991 .

[3]  Kuo-Yi Huang,et al.  Detection and classification of areca nuts with machine vision , 2012, Comput. Math. Appl..

[4]  G. Novak,et al.  An introduction to a vision system used for a MiroSOT robot soccer system , 2004, Second IEEE International Conference on Computational Cybernetics, 2004. ICCC 2004..

[5]  Baba C. Vemuri,et al.  Non-Lambertian Reflectance Modeling and Shape Recovery of Faces Using Tensor Splines , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Edwin R. Hancock,et al.  Lambertian reflectance correction for rough and shiny surfaces , 2002, Proceedings. International Conference on Image Processing.

[8]  Patrick Lambert,et al.  Numerical scheme for efficient colour image denoising , 2011, Comput. Math. Appl..

[9]  Jun Zhou,et al.  Design of vision system and recognition algorithm in Mirosot , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[10]  Chin-Hsing Chen,et al.  A fast method for robot location determination , 1989 .

[11]  Kuo-Liang Chung,et al.  An Efficient Randomized Algorithm for Detecting Circles , 2001, Comput. Vis. Image Underst..

[12]  Honge Ren,et al.  A new image segmentation method based on HSI color space for biped soccer robot , 2008, 2008 IEEE International Symposium on IT in Medicine and Education.

[13]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[14]  Yong-Huai Huang,et al.  Speed up the computation of randomized algorithms for detecting lines, circles, and ellipses using novel tuning- and LUT-based voting platform , 2007, Appl. Math. Comput..

[15]  Jonghyun Park,et al.  Color image segmentation using adaptive mean shift and statistical model-based methods , 2009, Comput. Math. Appl..

[16]  B. Ahirwal,et al.  FPGA based system for color space transformation RGB to YIQ and YCbCr , 2007, 2007 International Conference on Intelligent and Advanced Systems.

[17]  Navid Razmjooy,et al.  A real-time mathematical computer method for potato inspection using machine vision , 2012, Comput. Math. Appl..

[18]  Yong-Hwan Lee,et al.  Efficient object identification and localization for image retrieval using query-by-region , 2012, Comput. Math. Appl..

[19]  Bart Lamiroy,et al.  Robust Circle Detection , 2007 .