International Journal of Computer Applications (0975 – 8887) Volume 44– No.6, April 2012 15 Road Traffic Monitoring by Intelligence-Driven Window based Image Analysis Shivam Tyagi B.Tech, Department of Computer Science, College of Engineering Roorkee Vikas Tripathi Asst. Professor Department of Computer Science, Graphic Era University, Dehradun ABSTRACT The term Traffic Analysis is the process to identify the different aspects of traffic and resolve the problems in concerning areas. These traffic problems may belong to traffic congestion and in order to remove this traffic congestion, determination of Road Traffic Volume or Road Traffic Identification is expected. There are several techniques [6], [9], [11] of determining traffic volume on a specific road or highway such as Road Traffic Volume Detection by using LASER Sensors [11], Road Traffic Volume Detection by using inbuilt electromagnetic loops installed in roads and Road Traffic Volume Detection by using concepts of Digital Image Processing [3], [4], [5]. Now these techniques have their pro and cons. But by far the best technique, from the point of view of Expense and Results, is Digital Image Processing. The goal of this paper is to analyze the density of road traffic by using the techniques and methods of Digital Image Processing and we are achieving this goal by using an intelligent window based system which has the capability to enhance its power according to the need of the system. General Terms Digital Image Processing, Feature Extraction, Object Recognition, Pattern Recognition, Road Monitoring. Keywords Intelligence-driven window, Frames Generation, Identification of Vehicles, Video Regeneration, Problem Description, Parallel Vehicle. 1. INTRODUCTION 1.1 Traffic & its Analysis Traffic has its branches everywhere on roads in many forms. It may be in the form of vehicles, like cars, scooters, motorbikes, ridden animals, pedestrian and other conveyance. Basically it is the public way for purposes of travel. Some places may have consistently, extremely large traffic volume, either during periods of time referred to as rush hour or perpetually. Exceptionally, traffic jam can be the result of many situations like an accident or an obstruction, such as construction. Such dynamics in relation to traffic congestion is known as traffic flow. In measured traffic data, traffic congestion’s common spatiotemporal empirical features have been found and on analysis it is found that for different highways in different countries, it is qualitatively same. Wide moving jam and synchronized flow phases of congested traffic can be distinguished by some of these common features in Kerner’s three-phase traffic theory [1]. 1.2 Digital Image Processing The use of computer algorithms to perform image processing on digital images is Digital image processing. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. Digital Image Processing allows a much wider range of algorithms which can be applied on the input data and can avoid problems such as the build-up of noise and signal distortion during processing. Due to two dimensional (perhaps more) attribute of an image, digital image processing may be modeled in the form of multidimensional systems. In image processing, feature extraction has a great importance. Basically it is a special form of dimensionality reduction. When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant (much data, but not much information) then the input data will be transformed into a reduced representation set of features (also named features vector). Now feature extraction is to transform the input data into the set of features. The features set will extract the relevant information from the input data if the features extracted are carefully chosen. This features set is required to perform the desired task using this reduced representation instead of the full size input. The technique in this article focuses on the methods of digital image processing, computer vision algorithms and pattern recognition [10] to be applied to road traffic monitoring and analysis. Our main concern was to alter and modify these algorithms so that it can get fit into real-time road monitoring processes [9], and as a consequence the prototype system for traffic analysis was developed. Technically this system is based on stationary video cameras as well as computers connected to wide area network.. 2. PROBLEM DESCRIPTION Complex mathematical, algorithmic and programming problems were there when the techniques of image processing [7], [8] and object/pattern recognition [9] were applied on moving objects. Many articles have considered particular questions related: scene modeling, object geometry accounting, image contours processing. There is a lack of information on methods and algorithms used in digital monitoring technology, perhaps for commercial reasons.