An algorithm based on Haar-like features and AdaBoost classifier for vehicle recognition is proposed to solve the problem of poor recognition performance based on SVM (Support Vector Machines) classifier and cascaded AdaBoost classifier as well as the problem of much time consumed for training traditional AdaBoost.At first

the extended Haar-like features are extracted using integral image method

then a small number of critical features from a very large set of Haar-like features are selected while training AdaBoost

finally two classes classification is performed using the AdaBoost classifier and the selected features.Experimental results demonstrate that the proposed approaches has better performance both in recognition and time consuming than traditional methods

and shows promising perspective.