Abstract: In recent years, there has been a growing demand for intelligent transportation systems (ITS) to enhance road safety, traffic management, and law enforcement. This paper proposes an efficient Vehicle Monitoring System. The system comprises four primary components: vehicle speed estimation, number plate recognition, Deblurring number plate and moving vehicle detection.For vehicle speed estimation, by tracking vehicle movement and applying optical flow techniques, the system accurately calculates the speed of each passing vehicle in real-time.In parallel, the system integrates a Number Plate Detection module. Upon detecting vehicles within the camera's field of view, the system extracts their number plates using advanced object detection algorithms.The proposed method utilizes advanced image deblurring techniques to restore the clarity of vehicle number plates in blurred images. Initially, the system detects and extracts candidate regions containing number plates using state-of-the-art object detection algorithms. Also the proposed approach utilizes a convolutional neural network (CNN) architecture to detect and localize moving vehicles in video streams. By leveraging the temporal information inherent in consecutive frames, the system accurately distinguishes between static background elements and dynamic objects, such as vehicles in motion.Overall, the proposed system offers a robust and scalable solution for real-time vehicle speed and number plate detection, contributing to enhanced road safety, traffic management, and law enforcement in urban environments.
Index Terms: YOLO (You Only Look Once), CNN (Convolutional Neural Network), OCR(Optical Character Recognition).
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DOI:
10.17148/IJARCCE.2025.14361
