Optimally-Weighted Multi-Scale Local Feature Fusion Network for Driver Distraction Recognition

Abstract

Distracted driving is one of the main contributors to traffic accidents. In this work, we propose a novel multi-scale local feature fusion network for image-based distracted driver detection. Since the driver is the most important part to infer the distracted driver actions in a single image, our proposed method first detects the driver's body using person detection. Then capture abundant local body features after a repeated multi-scale feature fusion module. In addition to the features extracted from the whole image, our network also include the important feature of local body feature. The global feature and local feature are finally fused by an OAWS(optimally-weighted strategy). The experimental result shows that our methods achieve comparative performance on our own HY Large Vehicle Driver Dataset and the public AUC Driver Distracted Dataset.