Research & Implementation Of AdaBoost-Based Vehicle Recognition System

Along with the popularization of vehicles, traffic accidents have become one of the biggest social problems all over the world. Especially in China, the losses, which are caused by traffic accidents in both lives and economy, are startling. It is worth the whistle that about 85% of these accidents are due to human factors worldwide and even 95% in China. Therefore, it has become the key research direction in ITS (Intelligent Transportation System) field to promote the vehicle active security through improving object recognition performance and providing drivers with more alert and assistance information using the techniques of sensors such as video and radar sensors. On-road object recognition based on video sensors has become one of the focuses due to the low cost and the wide vision scene. At present, most methods of road object recognition based on vision follow two steps, hypothesis generation (HG) and hypothesis verification (HV). HG generates ROI (Regions of Interest) which include candidate objects; HV verifies the existence of object on ROI. Machine learning is one of the main methods of HV and has become an important research topic of vision-based road object recognition for its potential and availability.Currently, many machine learning based methods have been proposed for on-road object recognition. Processing flow of vehicle recognition using statistical pattern recognition in this paper is composed of three parts, feature extraction, feature selection and classification design. The feature extraction method is used to extract feature on the vehicle and background training or recognition image. AdaBoost algorithm is used to implement feature selection on the feature extraction results, then train the classifier. During the whole training process, AdaBoost is used both for feature selection and classifier training. At last the classifier trained by Adaboost for vehicle classification is applied. This method was tested under different test sets, and was compared with other vehicle recognition algorithms. The experiment results show that the algorithm can increase recognition rate and diminish error rate, and is more suitable for realistic application.