To the best of our knowledge, this is the first work that addresses the detection of aggressive driving behavior from traffic videos.
Aggressive driving (i.e., car drifting) is a dangerous behavior that puts human safety and life into a significant risk. In this paper, we targeted this issue and proposed DriftNet, a deep learning model to detect aggressive driving behavior automatically from videos. It is built upon a DenseNet 3D backbone. DrifNet has proven the ability to learn both spatial and temporal features related to car drifting by only training it on a weakly labeled dataset of traffic videos. We also created a dataset of car drifting in the Saudi Arabian context. The validation accuracy of DrifNet on this dataset was 77.5%, outperforming other tested algorithms. To the best of our knowledge, this is the first work that addresses the detection of aggressive driving behavior from traffic videos.
https://ieeexplore.ieee.org/abstract/document/9283799
The dataset used in the paper is provided in this link: https://drive.google.com/file/d/1MFIwgu7Jtxd9VIPuk3qDUh0zTNdsWgpu/view?usp=sharing
https://paperswithcode.com/paper/driftnet-aggressive-driving-behavior
If you find our work useful, please cite it:
@INPROCEEDINGS{9283799,
author={Noor, Alam and Benjdira, Bilel and Ammar, Adel and Koubaa, Anis},
booktitle={2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH)},
title={DriftNet: Aggressive Driving Behaviour Detection using 3D Convolutional Neural Networks},
year={2020},
volume={},
number={},
pages={214-219},
doi={10.1109/SMART-TECH49988.2020.00056}}