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类人足球机器人场上目标识别算法
引用本文:李丽丽,娄小平,吕乃光,林义闽. 类人足球机器人场上目标识别算法[J]. 计算机系统应用, 2014, 23(6): 86-92
作者姓名:李丽丽  娄小平  吕乃光  林义闽
作者单位:北京信息科技大学 仪器科学与光电工程学院, 北京 100192;北京信息科技大学 仪器科学与光电工程学院, 北京 100192;北京信息科技大学 仪器科学与光电工程学院, 北京 100192;北京邮电大学 光通信与光电子学院, 北京 100876
基金项目:北京市属高等学校人才强教计划(PXM2011_014224_113533);北京信息科技大学研究生科技创新项目(5028223205)
摘    要:在比赛过程中类人足球机器人的视觉系统需要对足球、球门以及对阵双方机器人进行识别. 考虑到算法的快速性及有效性,采用基于颜色信息的算法对球及球门进行识别,通过球及球门的颜色阈值提取图片中球与球门可能的位置,再由球与球门的背景色或面积信息确定球与球门的正确位置. 对双方机器人的识别,首先提取机器人的特征,然后通过在线实时的监督学习方法训练一组级联分类器,通过训练好的分类器对双方机器人进行检测. 实验表明算法能够快速有效地识别场上目标,且算法具有较好的鲁棒性.

关 键 词:类人足球机器人  目标识别  级联分类器
收稿时间:2013-10-19
修稿时间:2013-11-08

Objects Recognition Algorithm for Humanoid Robot Soccer on Playing Filed
LI Li-Li,LOU Xiao-Ping,LV Nai-Guang and LIN Yi-Min. Objects Recognition Algorithm for Humanoid Robot Soccer on Playing Filed[J]. Computer Systems& Applications, 2014, 23(6): 86-92
Authors:LI Li-Li  LOU Xiao-Ping  LV Nai-Guang  LIN Yi-Min
Affiliation:School of Instrumentation Science and Photoelectric Engineering, Beijing Information Science and Technology University, Beijing 100192, China;School of Instrumentation Science and Photoelectric Engineering, Beijing Information Science and Technology University, Beijing 100192, China;School of Instrumentation Science and Photoelectric Engineering, Beijing Information Science and Technology University, Beijing 100192, China;School of Optical Communication &Optoelectronics, Beijing University of Posts &Telecommunications, Beijing 100876, China
Abstract:During the race, the football, goal and two sides of the robot need to be recognized by the vision system of humanoid robot soccer. Considering the rapidity and effectiveness of the algorithm, the ball and the goal are identified by the algorithm based on color information. The possible positions of the ball and the goal are extracted by the color thresholds of the ball and the goal in the picture, then the correct position is determined by the background color or area information. To recognize the two sides of the robot, first the features of the robot should be extracted and then through online real-time supervision learning method a cascade classifier is trained, which is used to identify robots. Result shows that the field objects can be recognized quickly and effectively and the algorithm has better robustness.
Keywords:humanoid robot soccer  objects recognition  cascade classifier
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