Overloaded Vehicle Research Paper

A Survey on Detecting Overloaded Vehicle in Video Surveillance Systems Abstract

Overloaded vehicle is a challenging issue in public transport systems and is one of the major causes of road accidents. Vehicle which carry heavy load pose threat to human life expectancy and also cause excessive wear and damage to road, bridges, pavements and make the vehicle less stable. According to Motor Vehicle’s Act, overloading vehicle is an illegal offence which carries with fine and prison sentence. Even then, our community is least bothered about the same. Hence the need to address this problem is relevant in the present scenario. The large volume of vehicle on roads has been a challenge to authorities and manually monitoring

Delp[11], provides information about the vehicle which possess abnormal behavior. Anomalous behavior is detected by tracking the vehicle which constantly ignores the lane markings which could be a sign of driver’s negligence. A vehicle with visibly flat tires and moving below the speed limit could be overloaded and pose a threat to human life expectancy. Using two video cameras installed near signals relevant information about the vehicle is extracted using video analysis methods. Traditional Background subtraction method [2] is commonly used to identify moving vehicle. However this method is not effective in cases when background suddenly changes due to weather or other environmental changes. Satyam Srivastava et.al proposes a Motion Assisted Background Subtraction method used for identifying vehicles against an uncontrolled outdoor background. The front view camera helps in identifying the vehicle type, make as the vehicle enters the point of interest. The side view camera estimates the size of the tire by identifying the gap between the vehicle body and tires. It can be an indication of an overloaded vehicle, if the gap above the rear tire is smaller. One thousands of vehicles were experimented during fifteen minutes of analysis and detection failures were negligible. The system failed to detect vehicle where the foreground and background were similar and also when two vehicles move together were

Several factors need to be considered in monitoring passengers inside a bus. Such factors include switching seats among passengers, seating to standing and vice versa. In this scenario, it is not easy to count the number of passengers as it wouldn’t yield better results. The paper published by Boon Chong Chee[3] et.al proposes an elliptical head detection method count number of passengers in a bus using video surveillance systems. A human head is very suitable for elliptical matching as it is in the shape of an ellipse. Many researchers have contributed towards head detection method and it is explained in [ 9,12]. Boon Chong Chee et.al obtain the object boundaries used for head detection by applying edge detection method on an input image. This method did not give expected results in cases when boundaries of head in an edge image are shorter and are not continuous. Hence a template matching is carried out to locate head using a template of size of passenger’s head. Applying least square ellipse fitting algorithm to fabricated ellipse obtained for each matched region, the fitted ellipse residing in the original portion of fabricate is retained and using this head detection can be performed using the