Multi-model Fusion on Real-time Drowsiness Detection for Telemetric Robotics Tracking Applications

Drowsiness of driver is one of the common causes resulting in road crashes. According to the research, there have been twenty percent of the road accidents which are related to the drowsiness of drivers. Nowadays, with the development technology, various approaches are introduced to detect the drowsiness of drivers. In this paper, we propose a multi-model fusion system which is composed of the three models to capture driver's face and detect drowsiness in the real-time for telemetric robotics tracking applications. The sensor device we used is an RGB camera which is mounted in front of driver to obtain the facial image. Then, we combine the results based on the state of the eye blink, yawn and head deviation to determine whether the driver is drowsy. We test our models to obtain the weighting factors in drowsy value. In the experiment, we show that our system has the high accuracy of detection.

I. Introduction

Driver drowsiness is one of the major factors that causes several road accidents. The driver drowsiness accounts for approximately 20% in road crashes [17], [18]. Many tragedies can be avoided if we are able to alert the drivers when they are drowsy. Therefore, it is important to provide the method which can evaluate the state of drivers and alarm them while detecting the drowsiness.