  The dynamic and unpredictable nature of road traffic necessitates effective
accident detection methods for enhancing safety and streamlining traffic
management in smart cities. This paper offers a comprehensive exploration study
of prevailing accident detection techniques, shedding light on the nuances of
other state-of-the-art methodologies while providing a detailed overview of
distinct traffic accident types like rear-end collisions, T-bone collisions,
and frontal impact accidents. Our novel approach introduces the I3D-CONVLSTM2D
model architecture, a lightweight solution tailored explicitly for accident
detection in smart city traffic surveillance systems by integrating RGB frames
with optical flow information. Our experimental study's empirical analysis
underscores our approach's efficacy, with the I3D-CONVLSTM2D RGB + Optical-Flow
(Trainable) model outperforming its counterparts, achieving an impressive 87\%
Mean Average Precision (MAP). Our findings further elaborate on the challenges
posed by data imbalances, particularly when working with a limited number of
datasets, road structures, and traffic scenarios. Ultimately, our research
illuminates the path towards a sophisticated vision-based accident detection
system primed for real-time integration into edge IoT devices within smart
urban infrastructures.
