Due to continuing and rapid advances of both hardware and software technologies in camera and computing systems, we continue to have access to cheaper, faster, higher quality, and smaller cameras and computing units. As a result, vision based methods consisting of image processing and computational intelligence can be implemented more easily and affordably than ever using a camera and its associated operations units. Among their various applications, such systems are also being used more and more by researchers and practitioners as generic instruments to measure and monitor physical phenomena. In this article, we take a look at this rising trend and how cameras and vision are being used for instrumentation and measurement, and we also cast a glance at the metrological gauntlet thrown down by vision-based instruments.

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Camera as the instrument: the rising trend of vision based measurement

Driver drowsiness and distraction are two main reasons for traffic accidents and the related financial losses. Therefore, researchers have been working for more than a decade on designing driver inattention monitoring systems. As a result, several detection techniques for the detection of both drowsiness and distraction have been proposed in the literature. Some of these techniques were successfully adopted and implemented by the leading car companies. This paper discusses and provides a comprehensive insight into the well-established techniques for driver inattention monitoring and introduces the use of most recent and futuristic solutions exploiting mobile technologies such as smartphones and wearable devices. Then, a proposal is made for the active of such systems into car-to-car communication to support vehicular ad hoc network's (VANET's) primary aim of safe driving. We call this approach the dissemination of driver behavior via C2C communication. Throughout this paper, the most remarkable studies of the last five years were examined thoroughly in order to reveal the recent driver monitoring techniques and demonstrate the basic pros and cons. In addition, the studies were categorized into two groups: driver drowsiness and distraction. Then, research on the driver drowsiness was further divided into two main subgroups based on the exploitation of either visual features or nonvisual features. A comprehensive compilation, including used features, classification methods, accuracy rates, system parameters, and environmental details, was represented as tables to highlight the (dis)advantages and/or limitations of the aforementioned categories. A similar approach was also taken for the methods used for the detection of driver distraction.

Driver Behavior Analysis for Safe Driving: A Survey

In this paper, we present two video datasets of drivers with various facial characteristics, to be used for designing and testing algorithms and models for yawning detection. For collecting these videos, male and female candidates were asked to sit in the driver's seat of a car. The videos are taken in real and varying illumination conditions. In the first dataset, the camera is installed under the front mirror of the car. Each participant has three or four videos and each video contains different mouth conditions such as normal, talking/singing, and yawning. In the second dataset, the camera is installed on the dash in front of the driver, and each participant has one video with the above-mentioned different mouth conditions all in the same video. The car was parked for both datasets to keep the environment safe for the participants. As a benchmark, we also present the results of our own yawning detection method, and show that we can achieve a much higher accuracy in the scenario with the camera installed on the dash in front of the driver.

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YawDD: a yawning detection dataset

Drowsy driver alert systems have been developed to minimize and prevent car accidents. Existing vision-based systems are usually restricted to using visual cues, depend on tedious parameter tuning, or cannot work under general conditions. One additional crucial issue is the lack of public datasets that can be used to evaluate the performance of different methods. In this paper, we introduce a novel hierarchical temporal Deep Belief Network (HTDBN) method for drowsy detection. Our scheme first extracts high-level facial and head feature representations and then use them to recognize drowsiness-related symptoms. Two continuous-hidden Markov models are constructed on top of the DBNs. These are used to model and capture the interactive relations between eyes, mouth and head motions. We also collect a large comprehensive dataset containing various ethnicities, genders, lighting conditions and driving scenarios in pursuit of wide variations of driver videos. Experimental results demonstrate the feasibility of the proposed HTDBN framework in detecting drowsiness based on different visual cues.

Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network

Driver behavior monitoring system as Intelligent Transportation Systems (ITS) have been widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring the driver behavior are rely on computer vision techniques. Such methods suffer from violation of privacy and the possibility of spoofing. This paper presents a novel yet efficient deep learning method for analyzing the driver behavior. We have used the driving signals, including acceleration, gravity, throttle, speed, and Revolutions Per Minute (RPM) to recognize five types of driving styles, including normal, aggressive, distracted, drowsy, and drunk driving. To take the advantages of successful deep neural networks on images, we learn a 2D Convolutional Neural Network (CNN) on images constructed from driving signals based on recurrence plot technique. Experimental results confirm that the proposed method can efficiently detect the driver behavior.

Driver behavior detection and classification using deep convolutional neural networks

Fatigue and drowsiness of drivers are amongst the significant causes of road accidents. In this paper, we discuss a method for detecting drivers' drowsiness and subsequently alerting them. The aim is to reduce the number of accidents due to drivers fatigue and hence increase the transportation safety. Many special body and face gestures are used as sign of driver fatigue, including yawning, eye tiredness and eye movement, which indicate that the driver is no longer in a proper driving condition. Here, we propose a method of yawning detection based on the changes in the mouth geometric features.

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Driver drowsiness monitoring based on yawning detection

Yawning detection has a variety of important applications in a driver fatigue detection, well-being assessment of humans, driving behavior monitoring, operator attentiveness detection, and understanding the intentions of a person with a tongue disability. In all of the above applications, an automatic detection of yawning is one important system component. In this paper, we design and implement such automatic system, using computer vision, which runs on a computationally limited embedded smart camera platform to detect yawning. We use a significantly modified implementation of the Viola-Jones algorithm for face and mouth detections and, then, use a backprojection theory for measuring both the rate and the amount of the changes in the mouth, in order to detect yawning. As proof-of-concept, we have also implemented and tested our system on top of an actual smart camera embedded platform, called APEX from CogniVue Corporation. In our design and implementations, we took into consideration the practical aspects that many existing works ignore, such as real-time requirements of the system, as well as the limited processing power, memory, and computing capabilities of the embedded platform. Comparisons with existing methods show significant improvements in the correct yawning detection rate obtained by our proposed method.

Yawning Detection Using Embedded Smart Cameras

Driving while drowsy is a major cause behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of assistive systems that monitor a driver's level of vigilance and alert the driver in case of drowsiness can be significant in the prevention of accidents. This paper introduces the design and implementation of a lightweight system, in terms of computational complexity, which runs on a computationally-limited embedded smart camera platform to measure drivers' drowsiness based on yawning. Our system uses a modified implementation of the Viola-Jones algorithm for face and mouth detection, as well as the back projection theory for measuring both the rate and the amount of changes in the mouth for yawning detection. Our system is built on the top of an actual smart camera embedded platform, called APEX™ from CogniVue Corp., which is easy and practical for installation inside a car. The system is optimized in a way that meets the real time requirements of the monitoring task while relying on the limited processing power of the embedded platform.

A yawning measurement method using embedded smart cameras

In this demo we will present a vision-based smart environment using in-car cameras that can be used for real time tracking and monitoring of a driver in order to detect the driver's drowsiness based on yawning detection. As driver fatigue and drowsiness is a major cause behind a large number of road accidents, the assistive systems that monitor a driver's level of drowsiness and alert the driver in case of vigilance can play an important role in the prevention of such accidents. Our system is built on the top of an embedded platform, called APEX™ from CogniVue Corp., that is easy and practical for installation inside a car. Moreover, we have aimed at optimizing the system in a way that it meets the real time requirements of the monitoring task while relying on the limited processing power of the embedded platform.

Shabnam Abtahi

Papers

YawDD: a yawning detection dataset

Driver drowsiness monitoring based on yawning detection

Yawning Detection Using Embedded Smart Cameras

A yawning measurement method using embedded smart cameras

Demo: Vision based smart in-car camera system for driver yawning detection