Automated Traffic Monitoring and Accident Detection using Computer Vision

Traffic monitoring and accident detection are very important for ensuring road safety and optimizing traffic flow. Traditional methods rely heavily on manual observation and sensor-based systems, which can be inefficient and costly. In this paper, we propose an automated approach using computer vision techniques to enhance real-time traffic surveillance and incident detection. By integrating deep learning models with image and video processing, our system can accurately identify traffic congestion patterns, detect collisions, and alert emergency responders promptly. We explore various architectures, including a novel edge-tocloud integrated system that combines YOLOv8, DeepSORT, and CNN-Transformer fusion for real-time accident detection. This hybrid approach improves detection accuracy while maintaining low latency, making it well-suited for scalable deployment in intelligent transportation systems. The methodology for automatic traffic monitoring and accident detection using computer vision typically involves several key stages: Data Acquisition. Object Detection and Tracking, Accident Detection: Alert Generation and Response Data Analysis and Traffic Insights. Performance comparison of various computer vision models is also made in this paper.