A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …

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 …

Vision-based detection of road accidents using traffic surveillance video is a highly desirable
but challenging task. In this paper, we propose a novel framework for automatic detection of …

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 …

In this paper, a vision‐based crash detection framework was proposed to quickly detect
various crash types in mixed traffic flow environment, considering low‐visibility conditions …

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 …

Traffic accident detection is an important topic in traffic video analysis, and this paper
discusses single-vehicle traffic accident detection. Specifically, a novel real-time traffic …