Smart City Transposition: Deep Loaming Ensemble Approach For Traffic Detection

Abstract

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-ofthe-
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 I3DCONVLSTM2D
model architecture, a lightweight solution tailored explicitly for accident detection in smart
city traffic surveillance systems by integrating RGB frames with optical flow information. Empirical analysis
of our experimental study underscores the efficacy of our model architecture. The I3D-CONVLSTM2D RGB
+ Optical-Flow (trainable) model outperformed 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 realtime
integration into edge IoT devices within smart urban infrastructures.