As traffic surveillance technology continues to grow worldwide vehicle detection, counting, tracking and classification are gaining importance. This paper proposes computer vision based real time vehicle detection, tracking and classification at urban intersections. Firstly, foreground extraction using double subtraction method is proposed, which increases the accuracy of blob detection. Classification based on the geometrical attributes of the vehicle and also quadrant division of the junction is put forward. Setting up dynamic ROIs is discussed, which increased the scope of traffic surveillance for different types of junctions. The proposed system is implemented using Intel Open CV library for image processing and video processing applications. The Practical implementation of the algorithm is made with C++ and computer vision. Several junction surveillance videos are used to evaluate the performance of the traffic surveillance system. In this paper, detection, tracking and classification of objects in with removal of shadow and ghost vehicles at different junction in video surveillance .Proposed work elaborated computer vision approach for Traffic monitoring in traffic surveillance application. Test results in the performance of the proposed algorithm in detection, classification and counting and proved the effectiveness of the traffic surveillance system. Obtained results showed a better performance in terms of accuracy.