Vehicle detection and recognition for intelligent traffic surveillance system

Vehicle detection and type recognition based on static images is highly practical and directly applicable for various operations in a traffic surveillance system. This paper will introduce the processing of automatic vehicle detection and recognition. First, Haar-like features and AdaBoost algorithms are applied for feature extracting and constructing classifiers, which are used to locate the vehicle over the input image. Then, the Gabor wavelet transform and a local binary pattern operator is used to extract multi-scale and multi-orientation vehicle features, according to the outside interference on the image and the random position of the vehicle. Finally, the image is divided into small regions, from which histograms sequences are extracted and concentrated to represent the vehicle features. Principal component analysis is adopted to reach a low dimensional histogram feature, which is used to measure the similarity of different vehicles in euler space and the nearest neighborhood is exploited for final classification. The typed experiment shows that our detection rate is over 97 %, with a false rate of only 3 %, and that the vehicle recognition rate is over 91 %, while maintaining a fast processing time. This exhibits promising potential for implementation with real-world applications.