This research deals with measurement of the density of vehicles traffic. The traffic density is estimated from an image captured using the ordinary optical camera. An image processing methods is used and the edge of the objects is extracted. A two dimensional wavelet transform is used as a feature extraction. The extracted features were reduced by Multiple Region Centroid Estimation. A neural network is trained using many sets of images with different Traffic densities then it is used for traffic measurement. A classification rate of 98% can be achieved.