Real-Time Vehicles Speed Estimation

The requirement for precise and effective ways to track the speed of vehicles has grown in the fast-paced world of today. The precision and real-time monitoring of traditional speed measurement techniques, such radar guns and speed cameras, are limited. A new path for real-time vehicles speed measurement has emerged with the development of image processing tools. Through the use of computer vision, image processing techniques can extract useful information from visual data, such as pictures or video frames. Real-time accurate speed estimation of vehicles can be achieved by analyzing their motion patterns captured by cameras using advanced algorithms. This paper presents two methods for estimating the real-time speed of vehicles using image processing techniques. The first method utilizes the Lucas-Kanade algorithm, while the second method employs the Gaussian Mixture Model (GMM). Both approaches aim to automatically estimate the speed of vehicles from a real time sequence captured by a stationary camera. The performance of these methods for tracking of vehicles is evaluated as well in this study.