International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 05 | May-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1258 A Review on Automatic Traffic Monitoring System Soumen Bhowmik 1 , Anirban Halder 2 1 Assistant Professor, Department of Computer Science & Engineering, Bengal Institute of Technology & Management, Santiniketan, West Bengal, India 2 M. Tech Scholar, Department of Computer Science & Engineering, Bengal Institute of Technology & Management, Santiniketan, West Bengal, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In this paper we have reviewed different automated traffic monitoring systems. The continuous and automatic processing of data as it occurs in order to generate systematic output used to analyze system functions and ongoing procedures. Real time processing is critical to maintain proper functionality of automated or continuously operated systems. The user interface of a real-time system may use specialist input devices to provide data input. For example, a car driver will be providing input data to the onboard computer with throttle and brake pedals. A gamer may be using a joystick or hand held control to interact with the real- time game. Video sensors become particularly important in traffic applications mainly due to their fast response, easy installation, operation and maintenance, and their ability to monitor wide areas. Two of the most demanding and widely studied applications relate to traffic monitoring and automatic vehicle guidance. Key Words: Automatic Lane Finding, Video Sensor, edge- detection, real time processing, traffic monitoring. 1. INTRODUCTION The application of image processing and computer vision techniques to the analysis of video sequences of traffic flow offers significant improvements over the existing methods of traffic data collection and road traffic monitoring. Other methods suffer from serious drawbacks in that they are expensive to install and maintain and they are unable to detect slow or stationary vehicles. Video sensors provides wide area monitoring allowing analysis of traffic flows and turning movements, speed measurement, multiple point vehicle count. Image processing also finds wide applications in the related field of autonomous vehicle guidance, mainly for determining the vehicleǯs relative position in the lane and for obstacle detection. In this study we survey of algorithms and tools for the two major subtasks involved in traffic applications, i.e. the automatic lane finding (estimation of lane and/or central line) and vehicle detection (moving or stationary object/obstacle). 1.1 Automatic Lane Finding Stationary Camera: [8] A serious objective in the development of a road monitoring system based on image analysis is flexibility. The ability of the system to react to a changing scene while carrying out a variety of goals is a key issue in designing replacements to the existing methods of traffic data collection. This flexibility can be achieved only by a generalized approach to the problem which includes little or no a priori knowledge of the analyzed scene. Such a system will be able to adapt to Ǯchanging circumstancesǯ, which may include the following: changing light levels, i.e. night–day, or sunny–cloudy; deliberately altered camera scene, perhaps altered remotely by an operator. Automatic lane finding (ALF) is an important task for an adaptive traffic monitoring system. ALF can assist and simplify the installation of a detection system. It enables the system to adapt to different environmental conditions and camera viewing positions. It also enables applications in active vision systems, where the camera viewing angle and the focal length of the camera lens may be controlled by the system operator to find an optimum view. Moving Camera: [1][9][10][11][12][13]In the case of automatic vehicle guidance, the lane detection process is designed to (a) offer estimates for the position and orientation of the car within the lane and (b) conclude a reference system for locating other vehicles or obstacles in the path of that vehicle. Certain assumptions facilitate the lane detection task and/or speed-up the processing: i) Instead of processing entire images, a computer vision system can analyze specific regions ȋthe Ǯfocus of attentionǯȌ to identify and extract the features of interest. ii) The system can assume a fixed or smoothly varying lane width and thus limit its search to almost-parallel lane markings. iii) A system can abuse its knowledge of camera and an assumption of a precise 3D road model (for example, a flat road without bumps) to localize features easier and simplify the mapping between image pixels and their corresponding world coordinates. 1.2 Vehicle Detection [8]In road traffic monitoring, the video acquisition cameras are stationary. They are placed on posts above the ground to obtain optimal view of the road and the passing