Abstract
Edge detection is the most popular and commonly used technique for an image analysis. Much evaluation, experiments and deployment is done on edge detection .The major problem in edge detection technique is discontinuity in the image brightness in the different regions of an image. In order to avoid this particular issue, combination of two edge detection algorithms have been proposed, canny edge detection algorithm and Fuzzy based edge detection algorithm. In proposed method, along with tracking, classification of vehicles is also determined by using the distance Euclidean square algorithm and Kalman filter. The position of each vehicle will be estimated and tracked. The Kalman filter classifies detected vehicles in different specified groups and count them separately to provide useful information for traffic flow analysis. On observing the obtained results it is concluded that fuzzy canny edge detection has better efficiency than canny algorithm alone. This approach has provided improved results over the traditional canny edge detection technique based on Gaussian filter for noisy images.
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