Research On Vehicle Velocimetry System Based On Video

Intelligent Transport System is referred to as ITS. It is a multi-technology system, which includes information technology, data transmission technology, electronic sensor technology, electronic control technology and computer processing technology. It is a wide range, all-round, real-time, more accurate, more efficient integrated transport and management system. China’s intelligent transportation system has been used widely in a number of major cities but has not to reach a comprehensive range. Image processing technology develops very fast, which is bound to make a huge boost to the development of the ITS. The thesis is generated in such a large environment, which in order to study video velocimetry in video surveillance.Upon reviewing of the literature that is related to knowledge of ITS and learning video and image processing technology, this thesis designs a system of measuring vehicle speed based on monocular vision. The core idea of the system is to preprocess the frame factored out from the video and then the system can identify the vehicle in the case of guaranteeing recognition rate and velocity precision.The accuracy of tachometer depends on the accuracy of the measurement of distance. Monocular vision measuring distance needs to calibrate accurately some parameters in the traditional method, for example the focal length of the camera, installation height and mounting angle. The method is complicated, but the accuracy is also very difficult to meet the requirements. The thesis use rectangles to mark the identified vehicles. The vehicle’s external frame mark vehicles identified in this article, which renders a certain relationship between the Y coordinate of the cancroids of the rectangles and the vehicle away from the lens. By measuring the true value and the actual value of these two sets of data, then the thesis use the least squares method to fit unknown relationship, we can obtain connection weights. These connection weights are used as the polynomial coefficients. The distance between the car and the lens can be calculated by substituting the Y coordinate obtained in the image into the polynomial.The thesis applies preprocessing algorithm on the picture to eliminate random noise, which can get accurate identification of the vehicle. The using of the dual threshold can eliminate shadows, which aims to get accurate identification of the vehicle. According to background difference method, the system can track the vehicle detected. At last with a minimum bounding rectangle of the vehicle detected marks the vehicle detected.Practice has proved that this method is relatively simple, the accuracy can meet the requirements of the ITS.