Design and Implementation of Vehicle Speed Estimation Using Road Marking-based Perspective Transformation

Based on recent developments in speed estimation and deep learning technology, we prefer to use only one camera for speed estimation to reduce hardware costs, and calculate the speed immediately on the screen instead of calculating the average speed over a distance, making it easier to install the device. We use a machine learning method to detect vehicle objects, so as not to be limited by the direction of the vehicle. In addition, the road traffic marking rules can be used to obtain the size of all the road markings, which can be converted to a calibration matrix by road marking-based perspective transformation. Therefore, there is no need to measure the size of the road or get the scale of the image beforehand to calibrate and then complete the speed estimation. An average error is 3.2 km/hr, average relative error is 9.94%. In the future, we plan to improve the recognition accuracy and make the system lighter and cheaper by building it in an embedded system.