Traffic Rule Violation and Accident Detection using CNN

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Traffic Rule Violation and Accident Detection using CNN

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Abstract

Traffic rule violations and accidents are major sources of inconvenience and danger on the road. In this paper, we propose a convolutional neural network (CNN) based approach for detecting these events in real-time video streams. Our approach uses a YOLO-based object detection model to detect vehicles and other objects in the video, and an IOU-based accident detection module to identify potential accidents.We evaluate the performance of our approach on a large dataset of traffic video footage and demonstrate its effectiveness in detecting traffic rule violations and accidents in real-time. Our approach is able to accurately detect a wide range of traffic rule violations, including wrong-side driving, signal jumping, and over speed. It is also able to accurately track the movements of objects in the video and to identify potential accidents based on their trajectories.In addition to detecting traffic rule violations and accidents, our approach also uses an ANPR module to automatically read the license plate numbers of detected vehicles. This allows us to generate e-challans and punishments for traffic rule violations, providing a potential deterrent to future violations. Overall, our proposed approach shows promise as a tool for detecting and preventing traffic rule violations and accidents in real-time surveillance systems. By combining powerful object detection and motion analysis algorithms with an ANPR module, it is able to accurately and efficiently identify traffic rule violations and accidents, providing valuable information for traffic management and safety.

Keywords

Convolutional neural network (CNN) YOLO IOU Centroid tracking Deep Sort ANPR

References

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