License Plate-Location using Adaboost Algorithm
License Plate Recognition(LPR) is a very important research topic in computer vision of ITS. License plate location is the key step of LPR. Though numerous of techniques have been developed, most approaches work only under restricted conditions such as fixed illumination, limited vehicle license plates,and simple backgrounds. This paper attempts to use the AdaBoost algorithm to build up classifiers based on various features. Combining the classifiers using different features, we obtain a cascade classifier. Then the cascade classifier which consist of many layers of strong classifiers is implemented to locate the license plate.The training speed of the traditional AdaBoost Algorithm is slow. In order to increase the training speed, different features like derivative, texture are included. The classifiers based on the features we selected decrease the complexity of the system. The encouraging training speed is achieved in the experiments. Compared with other LPR method, for instance, color-based processing methods, our algorithm can detect the license plates with accurate sizes, positions and more complex backgrounds.
Adaboost Algorithm License Plate-Location Feature selection
Xiangdong Zhang Peiyi Shen Yuli Xiao Bo Li Yang Hu Dongpo Qi Xiao Xiao Liang Zhang
Xidian University,Xian,Shaanxi Province,China
国际会议
2010 IEEE信息与自动化国际会议(ICIA 2010)
哈尔滨
英文
1-6
2010-06-20（万方平台首次上网日期，不代表论文的发表时间）