Year: 2025
Type: article
Abstract: Effective accident detection techniques are essential for improving safety and expediting traffic management in smart cities due to the dynamic and unpredictable nature of road traffic. In addition to providing a thorough overview of various traffic accident types, such as rear-end collisions, T-bone collisions, and frontal impact accidents, this paper provides an in-depth investigation study of popular accident detection techniques, illuminating the subtleties of other cutting-edge approaches. By combining RGB frames with optical flow data, our innovative method presents the I3D-CONVLSTM2D model architecture, a lightweight solution specifically designed for accident detection in smart city traffic surveillance systems. Our experimental study's empirical analysis highlights how effective our model design is. With a remarkable Mean Average Precision (MAP) of 87%, the I3D-CONVLSTM2D RGB + Optical-Flow (trainable) model performed better than its competitors. Our results provide more insight into the difficulties caused by data imbalances, especially when dealing with a small number of datasets, road configurations, and traffic situations. In the end, our study shows the way to an advanced vision-based accident detection system that is ready for immediate incorporation into edge IoT sensors in smart city infrastructures.
Authors: Somireddypalli Maneesh Kumar, Bushra Tahseen
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409897880