In this paper we classify the vehicles into three main broad categories: A) Light vehicle B) Medium Vehicle C) Heavy Vehicle. Firstly, the image pre-processing is done with the gray-scale conversion and filtering of the image. Then with the help of fuzzy logic based novel edge detection technique we detect the edges to get the shape and size of the vehicle and classify the vehicle. | ISSN:2249-5789 Poonam Yadav et al, International Journal of Computer Science & Communication Networks,Vol 2(1), 42-45 VEHICLE CLASSIFICATION USING NOVEL EDGE DETECTION TECHNIQUE Poonam Yadav (Department of Computer Science) Lingayas University, Faridabad (Haryana.) ABSTRACT - Vehicle Class is an important parameter in road traffic management. With the help of vehicle classification the computation of percentage of state-aid streets, highways becomes simpler and it is also used in automated toll bridges. In this paper we classify the vehicles into three main broad categories: A) Light vehicle B) Medium Vehicle C) Heavy Vehicle. Firstly, the image pre-processing is done with the gray-scale conversion and filtering of the image. Then with the help of fuzzy logic based novel edge detection technique we detect the edges to get the shape and size of the vehicle and classify the vehicle. Keywords Edge Detection, Median Filter , Fuzzy Logic I. INTRODUCTION In this paper we classify the vehicle using fuzzy logic based novel edge detection technique. As it is known that Vehicle classification plays a key role in solving Traffic congestion problem. Current automatic vehicle classification systems have several deficiencies: low accuracy, special requirements, fixed orientation of the camera, or additional hardware and devices. In comparison with the existing systems, the major advantages of the proposed system are (a) no special orientation of the camera is required, (b) no additional devices are needed, and (c) high classification accuracy is provided. . Mandeep Kaur (Department of Computer Science) Lingayas University, Faridabad (Haryana.) The Sobel operator is a discrete differentaiation operator which computes an approximation of the gradient of the image intensity. In other words it can be said that, it uses intensity values only in a 3×3 region of each image point to approximate the corresponding image gradient, and it uses only integer values for the .
