Moving Vehicle Detection with Real Time Speed Estimation Using Deep Learning Techniques

Moving Vehicle Detection with Real-Time Speed Estimation and Number Plate Detection using OpenCV and YOLO is a system that can be used to automatically detect vehicles in a video stream, estimate their speed in real-time, and detect their number plates using computer vision techniques. The system uses the YOLO algorithm to detect and localize vehicles in the video frames. Once the vehicles are detected, their positions are tracked across multiple frames using object tracking algorithms such as Kalman filters or MOSSE. This allows the system to estimate the speed of the vehicles by measuring the distance they travel between consecutive frames and dividing by the time interval. If number plate detection is required, the system uses OCR algorithms to detect the characters on the number plates of the vehicles. This information can be used to identify and track specific vehicles, or to trigger an alert if a vehicle's number plate is associated with a specific event or situation. The system is typically implemented using OpenCV, an open-source computer vision library, and can run on a variety of hardware platforms, including desktop computers, embedded devices, or cloud servers. It can be used in a variety of applications, such as traffic monitoring, parking management, or law enforcement, to improve the efficiency and accuracy of vehicle detection, speed estimation, and number plate recognition. In this study, we offer a model for speed estimates and vehicle identification that uses yolo and OpenCV. The green box indicates speeds of 40 KM per hour or less, while the red box indicates speeds of 40 kilometers per hour or greater. Both the vehicle's speed and license plate will be determined by our model.