Abstract:

Vehicle recognition is one of the key technologies in the study of the intelligent transportation system. Aiming at the problems of a large amount of information to be managed, high dimension of extracted features and poor real-time recognition performance in the process of vehicle recognition, we propose a model recognition algorithm integrating PCA into LBP feature dimension reduction. Firstly, we use the inter-frame difference method to extract the target vehicle in the video sequence, then calculate the LBP characteristics of the target vehicle and utilize the PCA method to reduce the data dimension from pixel dimensions to training data dimensions. This algorithm can enhance its robustness to light variance and reduce the computation amount for vehicle recognition to some extent at the same time. Finally we use the minimum distance classifier to classify the vehicle model. Experimental results show that compared with the conventional PCA method, the proposed model recognition method has higher recognition accuracy when light changes and good real-time performance to a certain degree.

Key words: vehicle recognition, inter-frame difference method, feature dimension reduction, robustness, minimum distance classifier