Abstract: This paper presents a practical approach to detecting and estimating vehicle speeds in real time using video image processing techniques. The system utilizes object detection algorithms to identify vehicles in video frames and calculates their speeds by analyzing motion between consecutive frames. Designed to be both cost-effective and adaptable, this method provides a scalable solution for traffic monitoring and law enforcement applications. The backbone of the system lies in feature extraction and motion analysis, which are optimized to handle varying environmental conditions such as low lighting, adverse weather, and high traffic density. Frames extracted at regular intervals are preprocesses to enhance quality and reduce computational load, while convolutional neural networks (CNNs) enable the accurate detection of vehicles through learned spatial and temporal patterns. Speed estimation is achieved by calculating the displacement of detected vehicles across frames, with calibrations accounting for camera angles and dimensions. A key innovation of the proposed system is its modular architecture, allowing seamless integration with smart city ecosystems and IoT-enabled traffic infrastructures. The real-time processing capability of the system enables instant feedback for traffic regulation and speed enforcement, which can significantly reduce accidents and ensure road safety.
Keywords: Real-time vehicle speed detection, video image processing, object detection algorithms, convolutional neural networks (CNNs), motion analysis, traffic monitoring, speed estimation, modular architecture, IoT-enabled infrastructure, road safety enhancement.
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