Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection

TL;DR

This paper introduces a robust multi-feature two-stream network for driver drowsiness detection, addressing environmental susceptibility and achieving high accuracy by integrating temporal features, attention mechanisms, and adaptive histogram equalization.

Contribution

The paper proposes a novel multi-feature two-stream network with attention mechanisms for driver drowsiness detection, improving robustness against environmental factors and surpassing existing models in accuracy.

Findings

Achieved 94.46% accuracy on NTHU-DDD dataset.

Outperformed most existing fatigue detection models.

Effectively handled varying lighting conditions with CLAHE.

Abstract

Drowsiness driving is a major cause of traffic accidents and thus numerous previous researches have focused on driver drowsiness detection. Many drive relevant factors have been taken into consideration for fatigue detection and can lead to high precision, but there are still several serious constraints, such as most existing models are environmentally susceptible. In this paper, fatigue detection is considered as temporal action detection problem instead of image classification. The proposed detection system can be divided into four parts: (1) Localize the key patches of the detected driver picture which are critical for fatigue detection and calculate the corresponding optical flow. (2) Contrast Limited Adaptive Histogram Equalization (CLAHE) is used in our system to reduce the impact of different light conditions. (3) Three individual two-stream networks combined with attention mechanism are designed for each feature to extract temporal information. (4) The outputs of the three sub-networks will be concatenated and sent to the fully-connected network, which judges the status of the driver. The drowsiness detection system is trained and evaluated on the famous Nation Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset and we obtain an accuracy of 94.46%, which outperforms most existing fatigue detection models.