REAL TIME TRAFFIC SURVEILLANCE AND DETECTION USING DEEP LEARNING AND COMPUTER VISION TECHNIQUES

Abstract

The rapid growth of urbanization and the increasing number of vehicles on roads have made traffic management a critical challenge for modern cities. Traditional traffic monitoring systems rely heavily on manual observation and basic sensor-based technologies, which are often inefficient, error-prone, and unable to handle large-scale real-time data. This project proposes a Real-Time Traffic Surveillance and Detection System using advanced Deep Learning and Computer Vision techniques to improve traffic monitoring, congestion analysis, and incident detection. The primary objective of the system is to automatically detect and track vehicles, analyze traffic density, and identify violations or abnormal events in real time. The proposed system utilizes state-of-the-art deep learning models such as Convolutional Neural Networks (CNNs) and object detection algorithms like YOLO (You Only Look Once) for accurate vehicle detection and classification. Video data is captured from surveillance cameras and processed frame-by-frame using computer vision techniques such as image preprocessing, object tracking, and feature extraction. The system is capable of identifying different types of vehicles, counting traffic flow, and detecting anomalies such as accidents, over-speeding, or lane violations. Additionally, tracking algorithms are used to monitor vehicle movement across frames, enabling real-time analytics and decision-making. The performance of the system is evaluated using metrics such as accuracy, precision, recall, and processing speed. Experimental results demonstrate high detection accuracy and efficient real-time performance, making the system suitable for deployment in smart city environments. The system can assist traffic authorities in optimizing signal timings, reducing congestion, and improving road safety. Furthermore, it supports automated reporting and alerts, enhancing responsiveness to traffic incidents. Overall, the project highlights the potential of deep learning and computer vision in transforming traditional traffic management systems into intelligent and automated solutions.