Research Aspects - Driver Drowsiness Detection: Systems and Solutions - page 23

Chapter 4
Research Aspects
This chapter presents an overview of the research aspects associated with the
development of driver drowsiness detection (DDD) solutions. It summarizes
relevant technologies, popular algorithms, and design challenges associated with
such systems. It focuses particularly on vehicle-mounted solutions that perform
noninvasive monitoring of the driver's head and face for behavioral signs of
potential drowsiness, such as nodding, yawning, or blinking. Typically, systems
based on this methodology use a video camera for image acquisition and rely on a
combination of computer vision and machine learning techniques to detect events of
interest, measure them, and make a decision on whether the driver may be drowsy
or not. If the sequence of captured images and measured parameters (e.g., pattern
of nodding or time lapsed in “closed eye state”) suggest that the driver is drowsy,
an action—such as sounding an alarm—might be warranted.
DDD systems based on visual input are specialized computer vision solutions,
which capture successive video frames, process each frame, and make decisions
based on the analysis of the processed information. After capturing each frame
using an imaging sensor (Sect. 4.1 ), one or more feature detection and extraction
algorithms (Sect. 4.2 ) are applied to the pixel data. Their goal is to detect the
presence and location of critical portions of the image (e.g., head, face, and eyes),
measure their properties, and encode the results into numerical representations,
which can then be used as input by a machine learning classifier (Sect. 4.3 ) that
makes decisions such as “drowsy or not-drowsy” based on the analyzed data.
In the remainder of the chapter we discuss selected imaging sensors, feature
extraction algorithms, machine learning classifiers, and conclude by looking at
challenges and constraints associated with this field of research and development.