This work was carried out to determine the performance of image processing algorithms in classifying and counting moving vehicles in video streams of traffic scenes recorded by stationary cameras. The method for detection and tracking approach is as follows. The moving vehicles are first extracted from the traffic scene by applying the adoptive background substraction in order detect and count the vehicles using Gaussian Mixture Model. Isolated picture blobs are identified as individual vehicles after background subtraction using threshold and median filters. After the blobs have been identified, the vehicles in a given location are counted and classified. The preliminary results demonstrate that the developed system can track vehicles efficiently and reliably when a clear view of the traffic scene is available. For optimal camera calibration, an accuracy better than 80% in counting vehicles was recorded. The results of the developed system demonstrate that with additional enhancements, it may be utilized in real-time to count and classify vehicles on busy traffic routes