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-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 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

Published:

06-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1587-1591

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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