Self-Supervised Method for Risky Situation Detection in Road Traffic Sequences Using Video Masked Autoencoder

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.

@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},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Abdelhafid Berroukham and Mohammed Lahraichi and Khalid Housni},
  doi       = {10.14569/IJACSA.2025.0160654},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160654}
}