REAL-TIME VEHICLE DETECTION AND NUMBER PLATE RECOGNITION

Abstract

Road planning and traffic management issues can be resolved with the help of dynamic vehicle detection and tracking. Real-time traffic monitoring is a difficult task. The tedious, expensive, timeconsuming, and labor-intensive processes used in traditional traffic monitoring include human operators. In surveillance on video streams generated by traffic monitoring and surveillance cameras, it is now possible to use object detection and counting, behavioural analysis of traffic patterns, and number plate recognition. This is used to identify a vehicle specifically. It belongs to the field of image processing. Calculating traffic congestion on highways requires accurate vehicle identification, counting the number of cars on the road, and knowledge of the direction of traffic in a given region. The goal of this project is to create a system that analyses traffic camera footage and produces reports. Here, the focus of our project is on counting vehicles and detecting licence plates in streaming video. Python is an image processing technology used in our Framework.The suggested model is a real-time, precise vehicle detector, which makes it perfect for computer vision applications, according to experimental data.