HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

Qaisar Abbas

doi:10.14569/ijacsa.2020.0110173

Abstract

Road accidents mainly caused by the state of driver drowsiness. Detection of driver drowsiness (DDD) or fatigue is an important and challenging task to save road-side accidents. To help reduce the mortality rate, the “HybridFatigue” DDD system was proposed. This HybridFatigue system is based on integrating visual features through PERCLOS measure and non-visual features by heart-beat (ECG) sensors. A hybrid system was implemented to combine both visual and non-visual features. Those hybrid features have been extracted and classified as driver fatigue by advanced deep-learning-based architectures in real-time. A multi-layer based transfer learning approach by using a convolutional neural network (CNN) and deep-belief network (DBN) was used to detect driver fatigue from hybrid features. To solve night-time driving and to get accurate results, the ECG sensors were utilized on steering by analyzing heartbeat signals in case if the camera is not enough to get facial features. Also to solve the accurate detection of center head-position of drivers, two-cameras were mounted instead of a single camera. As a result, a new HybridFatigue system was proposed to get high accuracy of driver's fatigue. To train and test this HybridFatigue system, three online datasets were used. Compare to state-of-the-art DDD system, the HybridFatigue system is outperformed. On average, the HybridFatigue system achieved 94.5% detection accuracy on 4250 images when tested on different subjects in the variable environment. The experimental results indicate that the HybridFatigue system can be utilized to decrease accidents.

Highlights

Driver fatigue detection (DDD) is one of the main challenging tasks to save road accidents especially in the case of Saudi Arabia where highway driving is a very common way of traveling
The HybridFatigue detection system was developed in this paper without performing any pre- or post-image processing techniques so that it can be used as real-time environment
Compare to state-of-the-art driver drowsiness detection (DDD) system, an improved system was developed in this paper, which is based on a pre-train convolutional neural network (CNN) and deep-belief network (DBN)

Summary

Introduction

Driver fatigue detection (DDD) is one of the main challenging tasks to save road accidents especially in the case of Saudi Arabia where highway driving is a very common way of traveling. Several intelligent DDD-systems are examined in the past studies to enable the identification of driver fatigue, which enables to build the future systems Many conventional algorithms such as Fuzzy logic, statistical models and decision trees as well as modern deep learning algorithms are described and compared. Those intelligent algorithms for DDD systems are widely utilized [6] in the past automatic systems to predict driver fatigue. Visual features are extracted using multi-cams, heart-beats are measured through ECG sensors and those hybrid features are classified using transfer deep-learning approach Those algorithms are usually implemented and tested through a set of rules or models to recognized drivers state during driving in various conditions. The authors have combined visual and non-visual based approached together for getting higher accuracy compared to non-hybrid methods as described in the subsequent subsection

Objectives

Methods

Results

Conclusion