Design of an End-to-End Dual Mode Driver Distraction Detection System

This paper provides initial results on developing a deep neural network-based system for driver distraction detection which is operational at daytime as well as nightime. Unlike other existing methods that rely on only RGB images for daytime detection, the proposed system consists of two operating modes. The daytime mode uses a convolutional neural network to classify drivers' states based on their body poses in RGB images. The nighttime mode classifies Near Infrared images using a different neural network-based model and trained under different circumstances. To the best of our knowledge, this is the first work that explicitly addresses driver behavior detection at night using end-to-end convolutional neural networks. With initial experimental results, we empirically demonstrate that, with a relatively modest model complexity, the proposed system achieves high performance on driver distraction detection for both modes. Furthermore, we discuss the feasibility of developing a system with a small footprint and design structure but accurate enough to be deployed on a memory-restricted computing platform environment.