An Ensemble Deep Learning Approach for Traffic Accident Detection in Smart City Transportation Systems

Abstract: The system is designed to work with live video feeds from cameras installed in strategic locations. It employs object detection algorithms to identify and track vehicles in real-time, allowing for accurate traffic analysis. The system incorporates speed violation detection by defining speed limit lines and calculating the speed of vehicles passing through those lines. Violation instances are flagged, and images or videos of the violations are captured for further analysis or evidence purposes. The project also includes a user-friendly interface that provides real-time traffic statistics, including the total number of vehicles, traffic congestion levels, and detected violations. Additionally, the system offers configurable settings for road-specific parameters, such as speed limits and the number of allowed vehicles. The proposed system aims to enhance traffic management and improve road safety by providing timely and accurate information to authorities. It can aid in monitoring traffic patterns, identifying congested areas, and enforcing speed limits. The system has the potential to reduce accidents, enhance traffic flow, and contribute to efficient transportation management. Overall, the project showcases the effective utilization of computer vision and deep learning algorithms to develop a comprehensive traffic monitoring and violation detection system that can significantly impact road safety and traffic management.

Keywords: Traffic Management, Traffic Flow, Deep Learning Algorithms, Traffic Monitoring, Computer Vision.