Intelligent Real-Time Traffic Monitoring Using Deep Learning and Computer Vision

Abstract

To enhance safety, efficiency, and security, modern transportation systems increasingly depend on real-time traffic monitoring and detection. This study proposes an innovative approach that leverages deep learning and computer vision techniques to achieve accurate, real-time traffic surveillance. By utilizing advanced object detection algorithms and convolutional neural networks (CNNs), the system effectively identifies and tracks moving objects in traffic camera feeds. To adapt pre-trained CNN models to the specific requirements of traffic monitoring, the framework incorporates cutting-edge methods such as data augmentation and transfer learning. Additionally, it integrates features for crowd density estimation, anomaly detection, and traffic flow analysis, all of which contribute to improved traffic management and decision-making. Extensive experiments conducted on real-world traffic datasets demonstrate that the proposed approach surpasses traditional methods in terms of detection accuracy, processing speed, and scalability. This research contributes significantly to the field of intelligent transportation systems by offering a robust and efficient solution for real-time traffic observation, with potential applications in public safety, congestion control, and urban traffic monitoring.