Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework

Abstract

We propose a condition-adaptive representation learning framework for the driver drowsiness detection based on 3D-deep convolutional neural network. The proposed framework consists of four models: spatio-temporal representation learning, scene condition understanding, feature fusion, and drowsiness detection. The spatio-temporal representation learning extracts features that can describe motions and appearances in video simultaneously. The scene condition understanding classifies the scene conditions related to various conditions about the drivers and driving situations such as statuses of wearing glasses, illumination condition of driving, and motion of facial elements such as head, eye, and mouth. The feature fusion generates a condition-adaptive representation using two features extracted from above models. The detection model recognizes drivers drowsiness status using the condition-adaptive representation. The condition-adaptive representation learning framework can extract more discriminative features focusing on each scene condition than the general representation so that the drowsiness detection method can provide more accurate results for the various driving situations. The proposed framework is evaluated with the NTHU Drowsy Driver Detection video dataset. The experimental results show that our framework outperforms the existing drowsiness detection methods based on visual analysis.

IEEE Transactions on Intelligent Transportation Systems publication information (2018)

References in corpus (6)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Two-Stream Convolutional Networks for Action Recognition in Videos
• Learning Deep Representation for Face Alignment with Auxiliary Attributes
• Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

Cited by in corpus (6)

• Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving
• Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles
• Behavioral Research and Practical Models of Drivers' Attention
• SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition
• Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognition
• Boosting Network Weight Separability via Feed-Backward Reconstruction