A lightweight ConvGRU network for Distracted Driving detection
Pantazis Anagnostou, Nikolaos Mitianoudis · 2023
In this paper, we explore the problem of automatic detection of dangerous or distracted driving using multi-modal cameras. A deep convolutional model with Gated Recurrent Unit (GRU) layers for classification on the Driver Anomaly Detection (DAD) dataset is proposed. The key features are the limited use of 3D convolutions and the replacement of 2D convolutions with depth-wise separable convolutions, which reduce the computational complexity of the model to a small fraction of previous architectures with a small decrease in AUC performance. In addition, the threshold for binary classification between safe and distracted driving is adaptively estimated through the training data. Finally, an ensemble of all multi-modal inputs yields the final classification with favourable performance.