Deep Learning-Based Anomaly Detection System for Automatic Traffic Identification and Localization

Global data indicates that road crashes cause a significant portion of violent deaths. The human element significantly impacts the time it takes to dispatch the medical response to the scene and is correlated with the likelihood of survival. To improve public safety by finding and preventing deadly crashes, it is imperative to monitor traffic surveillance footage efficiently. Nevertheless, manual oversight of these movies is time-consuming, prone to mistakes, and ineffective. In response to this challenge, we present an anomaly detection problem-based deep learning system for automatically identifying and locating traffic incidents. To successfully collect and evaluate both time and space-based trends in video data, our technique uses a one-class classification framework that integrates spatial-temporal features and a mixture of Convolutional Neural Networks (CNNs) with the YOLO (You Only Look Once) model. Our approach can identify accident occurrences by concentrating on anomalous patterns that diverge from regular traffic behavior. Several real-world traffic surveillance datasets are used to assess the model, and the results show notable gains in qualitative and quantitative performance indicators. Compared to conventional methods, the results demonstrate how effective our methodology is at improving detection effectiveness and lowering the number of positive results. This approach marks a substantial leap in traffic surveillance by providing a scalable and reliable real-time accident identification and localization solution. It could enhance emergency response times and traffic management systems.