A Non-Intrusive Method for Detecting Visual Distraction Indicators of Transport Network Vehicle Service Drivers Using Computer Vision

Castro, Michael Jay C. De; Medina, Joshua Rodgregor E.; Lopez, John Paul G.; Goma, Joel C. De; Devaraj, Madhavi

doi:10.1109/hnicem.2018.8666306

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Several researchers have also been working on deep learning techniques to detect distracted driving. Liang and Lee ( 32 ) achieved 88% accuracy using a hybrid Bayesian model, while De Castro et al ( 44 ) achieved 89% using OpenFace—a feature extraction software. Eraqi et al ( 54 ) proposed a genetically weighted ensemble of convolutional neural networks (CNNs), producing a reliable deep learning-based system with a detection accuracy of 90%.…”

Section: Discussionmentioning

confidence: 99%

“…Streiffer et al ( 43 ) created a unified data analysis framework called DarNet to collect and analyze images of distracted test drivers, finding an increased classification accuracy compared with existing baseline models. De Castro et al ( 44 ) used cameras inside the car to track the eye gaze of drivers using the OpenFace model, and achieved a detection accuracy of 84% for distracted driving. Tran et al ( 8 ) used dual (front and side) cameras on a driving testbed to capture driver images, and they achieved a detection accuracy of 97%.…”

Section: Data Collectionmentioning

confidence: 99%

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Distracted Driving Crashes: A Review on Data Collection, Analysis, and Crash Prevention Methods

Hasan

Jalayer

Heitmann

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Distracted driving is one of the top three reasons for traffic fatalities. Every year, thousands of people are injured or killed in motor vehicle crashes resulting from distracted driving and recent technological advancements have increased the sources and frequency of distractions. This study provides a comprehensive literature review and a summary of findings for identifying best practices to collect and analyze data on distracted driving and countermeasures to mitigate distracted driving. It identifies literature published since 2006 that focuses exclusively on distracted driving. The results found that the severity of crashes involving distracted driving depends primarily on driver behavior and the geometric design of roadway and temporal variables. It was also found that several techniques exist to collect driver behavior data using dashcam cameras integrated into the dashboard of vehicles. For the detection of distracted driving, deep learning techniques are most often used by researchers. It is also found that the integration of the three Es approach in countermeasures is needed to mitigate distracted driving. These findings will help decision-makers comprehend the significant contributing factors associated with crashes involving distracted driving and implement the necessary data collection, data analysis, and practical treatments to reduce the crash severity. Based on the literature review findings, future research recommendations to address distracted driving are proposed.

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Section: Discussionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Distracted Driving Crashes: A Review on Data Collection, Analysis, and Crash Prevention Methods

Hasan

Jalayer

Heitmann

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…The video cameras are installed inside the vehicle at suitable locations to record the driver's physical movements and gaze data. The main advantage of video-based gaze detection approaches lies with its nonintrusive nature [180][181][182][183]. For instance, the authors of [176] modeled and detected a driver's visual distraction using the information associated with pose and position of the driver's head.…”

Section: Measurement Approachesmentioning

confidence: 99%

Gaze and Eye Tracking: Techniques and Applications in ADAS

Khan

Lee

2019

Sensors

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Tracking drivers’ eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers’ community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories. The advantages and limitations of each category are explained with respect to their requirements and practical uses. In another section of the paper, the applications of eyes and gaze tracking systems in ADAS are discussed. The process of acquisition of driver’s eyes and gaze data and the algorithms used to process this data are explained. It is explained how the data related to a driver’s eyes and gaze can be used in ADAS to reduce the losses associated with road accidents occurring due to visual distraction of the driver. A discussion on the required features of current and future eye and gaze trackers is also presented.

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Distraction detection of lectures in e-learning using machine learning based on human facial features and postural information

Betto

Hatano

Nishiyama

2022

Artif Life Robotics

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A Non-Intrusive Method for Detecting Visual Distraction Indicators of Transport Network Vehicle Service Drivers Using Computer Vision

Cited by 4 publications

References 6 publications

Distracted Driving Crashes: A Review on Data Collection, Analysis, and Crash Prevention Methods

Distracted Driving Crashes: A Review on Data Collection, Analysis, and Crash Prevention Methods

Gaze and Eye Tracking: Techniques and Applications in ADAS

Distraction detection of lectures in e-learning using machine learning based on human facial features and postural information

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