Computer vision has evolved in the last decade as a key technology for numerous
applications replacing human supervision. Timely detection of traffic violations and …

Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we
use eXtreme Gradient Boosting (XGBoost)—a Machine Learning (ML) technique—to detect …

Automatic recognition of road accidents in traffic videos can improve road safety. Smart cities
can deploy accident recognition systems to promote urban traffic safety and efficiency. This …

Almost all of the automatic accident detection (AAD) system suffers from the tradeoff
between computational overhead and detection accuracy. Recent advances in detection …

Camera-based systems are increasingly used for collecting information on intersections and
arterials. Unlike loop controllers that can generally be only used for detection and movement …

According to worldwide statistics, traffic accidents are the cause of a high percentage of
violent deaths. The time taken to send the medical response to the accident site is largely …

For the past few decades, automatic accident detection, especially using video analysis, has
become a very important subject. It is important not only for traffic management but also, for …

Traditional highway safety and monitoring solutions, reliant on surveillance cameras, face
limitations due to their dependence on high-speed internet connectivity and the remote …

Vehicular accident prediction and detection has recently garnered curiosity and large
amounts of attention in machine learning applications and related areas, due to its peculiar …

Quick and accurate crash detection is important for saving lives and improved traffic incident
management. In this paper, a feature fusion‐based deep learning framework was developed …