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Aggressive driving (i.e., car drifting) is a dangerous behavior that putshuman safety and life into a significant risk. This behavior is considered asan anomaly concerning the regular traffic in public transportation roads.Recent techniques in deep learning proposed new approaches for anomalydetection in different contexts such as pedestrian monitoring, street fighting,and threat detection. In this paper, we propose a new anomaly detectionframework applied to the detection of aggressive driving behavior. Ourcontribution consists in the development of a 3D neural network architecture,based on the state-of-the-art EfficientNet 2D image classifier, for theaggressive driving detection in videos. We propose an EfficientNet3D CNNfeature extractor for video analysis, and we compare it with existing featureextractors. We also created a dataset of car drifting in Saudi Arabian contexthttps://www.youtube.com/watch?v=vLzgye1-d1k . To the best of our knowledge,this is the first work that addresses the problem of aggressive drivingbehavior using deep learning.

DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture