An image vehicle classification method based on edge and PCA applied to blocks

Automatic vehicle classification is an important task in Intelligent Transport System (ITS) because it allows to obtain the traffic parameter called vehicles count by category. In terrestrial public roads variants sources of information, for vehicles counter by category, have been used such as video, magnetic induction coil, sound sensors, temperature sensors and microwave. The use of video has increased support for traffic management due to the advantages of installation cost and a wide range of information it contains. However, proposed methods of images vehicles classification, obtained from videos of roads traffic, have known limitations, such as strong dependence of detection methods, hard image normalization, noise and low accuracy. This paper presents a method for image vehicle classification based on road traffic video, whose objectives are easy normalization and acceptable accuracy. The method consists of three stages: normalization, training and classification. The images were obtained from road traffic video, taken during a summer day. Edge and PCA like features and the Adaptive-KNN like distance were used for classification. The experimental platform is built on Matlab R2009a.