Vehicle Detection and Counting System

In traffic surveillance systems, where effective traffic control and security are the main considerations, vehicle recognition and tracking play a vital and effective role. We explore and handle the problem of extracting vehicle and traffic information from video frames in this report. Even though numerous studies have been conducted and numerous approaches have been used, there is still potential for advancement in this field. Despite the fact that numerous studies and methodologies have been adopted in this field, there is still space for improvement. For observing, Organising, and managing traffic flow, passing car identification, examining, and counting are vital. In contrast to previous methods, a video-based solution is simple to deploy and does not impede traffic. In this project, a video-based solution is developed after analysing a series of traffic footage captured by a camera and using adaptive subtracted background technology, virtual detector technology, and blob tracking technology. Experiments were carried out using Visual Python code and OpenCV development kits, and the results show that the proposed method may precisely identify, follow, and quantity moving autos.