Automatic Moving Object Extraction For Traffic Monitoring Systems

Using a stochastic approach, this paper explores and models the basic stochastic characteristics of freeway traffic behavior under a wide range of traffic conditions during peak Periods and then applies the models to short-term traffic speed prediction.Automated motion detection has become an increasingly important subject in traffic surveillance systems. Automatic video analysis from traffic surveillance cameras is a fast-emerging field based on computer vision techniques. It is a key technology to public safety, intelligent transport system (ITS) and for efficient management of traffic. In recent years, there has been an increased scope for automatic analysis of traffic activity. We define video analytics as computer-vision-based surveillance algorithms and systems to extract contextual information from video. In traffic scenarios several monitoring objectives can be supported by the application of computer vision and pattern recognition techniques, including the detection of traffic violations (e.g., illegal turns and one-way streets) and the identification of road users (e.g., vehicles, motorbikes, and pedestrians). Currently most reliable approach is through the recognition of number plates, i.e., automatic number plate recognition (ANPR), which is also known as automatic license plate recognition (ALPR), or radio frequency transponders. Keywords— IR (infrared)sensor, Image processing, Micro-controller, Digital Display. INTRODUCTION The escalating increase of contemporary urban and national road networks over the last three decades emerged the need of efficient monitoring and management of road traffic. Conventional techniques for traffic measurements, such as inductive loops, EM microwave detectors, suffer from serious shortcomings, expensive to install, they demand traffic disruption during installation or maintenance, they are bulky and they are unable to detect slow or temporary stop vehicles. On the contrary, systems that are based on video are easy to install, use the existing infrastructure of traffic surveillance. Furthermore, they can be easily upgraded and they offer the flexibility to redesign the system and its functionality by simply changing the system algorithms. Those systems allow measurement of vehicle’s speed, counting the number of vehicles, classification of vehicles, and the identification of traffic incidents (such as accidents or heavy congestion).There is a wide variety of systems based on video and image processing employing different methodologies to detect vehicles and objects. These systems are intelligent enough to automatically track, classify, and recognize the object. In addition, it intelligently detects the suspicious behavior and does the activity recognition of the object. In urban environment, monitoring congestion across the road, vehicle interaction and detection of traffic rule violation can be done with visual surveillance systems. The Video surveillance system can prevent serious accidents, so that precious lives can be saved. ANPR is a very specialized wellresearched application for video analytics. Toll stations of freeways have dedicated lanes with cameras, where registered users can slowly pass without stopping. In contrast, inner city congestion charge systems (e.g., Stockholm, Sweden; London, U.K., and Singapore) have to be less intrusive and operate on the normal flow of passing traffic (freeflow tolling). For most traffic surveillance systems there are three major stages which are used for estimation of desired traffic parameters i.e. vehicle detection, tracking, and classification. For detection of vehicles, most of the methods assume that the camera is static and then desired vehicles can be detected by image differencing. Tracking is a very important issue in computer vision. Recently, there is a deep interest in surveillance applications. The main purpose of tracking in computer vision is to recognize and locate a prototype in a series of sequential frames. Many applications are based on Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-4, 2016 ISSN: 2454-1362, http://www.onlinejournal.in Imperial Journal of Interdisciplinary Research (IJIR) Page 1069 tracking such as video processing, security, surveillance and automatic procedures. Then, different tracking scheme are designed to track each vehicle. After that, several vehicle features like shape, length, width, texture, license number etc., are extracted for vehicle classification. License plate recognition License plate recognition (LPR) is one form of ITS (Intelligent Transport System) technology that not only recognizes and counts the number of vehicles but also differentiates them. For some applications, such as electronic toll collection and red-light violation enforcement, LPR records license plates alphanumerically so the vehicle owner can be assessed the appropriate amount of fine. In others cases, like commercial vehicle operations or secure-access control, a vehicle's license plate is compared against a database of acceptable ones to determine whether a truck can bypass a weigh station or a car can enter a gated community or parking lot. A license plate is the unique identification of a vehicle. The basic issues in real-time license plate recognition are the accuracy and the recognition speed. License Plate Recognition (LPR) has been applied in numerous applications such as automatically identifying vehicles in parking lots, access control in a restricted area and detecting and verifying stolen vehicles. Quality of algorithms used in a license plate detector determines the speed and accuracy of the license plate detection. In the past, a number of techniques have been proposed for locating the plate through visual image processing. Fig1. visual image processing A video is taken from a camera, and then each frame of the video is processed as the image. In this stage the license plate region from the given image is located and isolated. Quality of the image plays an important part hence prior to this stage preprocessing of the image is necessary. So first each frame pre-processed by binarization, noise reduction and edge detection. Then, the license plate is located by different image processing technique.