Abstract: Road traffic accidents are a significant public health issue, particularly in developing nations, where infrastructure and traffic monitoring systems may be limited. Risky situations including sudden stopping, lane switching, and near-misses can lead to accidents. In this study, we present an original approach for recognizing risky situations in road traffic sequences using Video Masked Autoencoder (VideoMAE), a self-supervised deep learning model built upon Vision Transformer architecture. By applying a pre-trained VideoMAE on a large dataset of videos and fine-tuning it on labeled traffic sequences categorized as risky or non-risky, our model learns spatiotemporal features without requiring extensive manual labeling. The method achieves high accuracy on testing data, demonstrating strong potential for high-risk detection with an accuracy of 95%. This study highlights the promise of self-supervised video representation learning for real-world safety applications and paves the way for the development of intelligent traffic monitoring and crash prevention tools.

Abdelhafid Berroukham, Mohammed Lahraichi and Khalid Housni, “Self-Supervised Method for Risky Situation Detection in Road Traffic Sequences Using Video Masked Autoencoder” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160654

@article{Berroukham2025,
title = {Self-Supervised Method for Risky Situation Detection in Road Traffic Sequences Using Video Masked Autoencoder},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160654},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160654},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Abdelhafid Berroukham and Mohammed Lahraichi and Khalid Housni}
}