In-the-wild Drowsiness Detection from Facial Expressions

Joshi, Ajjen; Kyal, Survi; Banerjee, S.; Mishra, Taniya

doi:10.1109/iv47402.2020.9304579

Cited by 23 publications

(16 citation statements)

References 22 publications

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“…Eye feature detection can be used as a non-invasive alternative of EOG for measuring blink duration, blink frequency and percentage of eyelid closure (PERCLOS), where the general measure is where the eye is 80% closed. Of the behavioural indicators, two studies reported that head movement/pose features provide the highest correlation with drowsiness [117], [118]; however, eye movement-based measures are the most widely used measures [14].…”

Section: Behavioural Methodsmentioning

confidence: 99%

Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation

Perkins¹,

Sitaula²,

Burke³

et al. 2021

Preprint

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Driver drowsiness has caused a large number of serious injuries and deaths on public roads and incurred billions of taxpayer dollars in costs. Hence, monitoring of drowsiness is critical to reduce this burden on society. This paper surveys the broad range of solutions proposed to address the challenges of driver drowsiness, and identifies the key steps required for successful implementation. Although some commercial products already exist, with vehicle-based methods most commonly implemented by automotive manufacturers, these systems may not have the level of accuracy required to properly predict and monitor drowsiness. State-of-the-art models use physiological, behavioural and vehicle-based methods to detect drowsiness, with hybrid methods emerging as a superior approach. Current setbacks to implementing these methods include late detection, intrusiveness and subject diversity. In particular, physiological monitoring methods such as Electroencephalography (EEG) are intrusive to drivers; while behavioural monitoring is least robust, affected by external factors such as lighting, as well as being subject to privacy concerns. Drowsiness detection models are often developed and validated based on subjective measures, with the Karolinska Sleepiness Scale being the most popular. Subjective and incoherent labelling of drowsiness, lack of on road data and inconsistent protocols for data collection are among other challenges to be addressed to progress drowsiness detection for reliable on-road use.

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Section: Behavioural Methodsmentioning

confidence: 99%

Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation

Perkins¹,

Sitaula²,

Burke³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The lack of benchmarks and publicly available naturalistic data make it difficult to establish state-of-the-art performance for drowsiness detection and their suitability for practical use, respectively. Despite reporting excellent results, many algorithms still struggle with certain aspects of the problem, notably extreme head angles [144], [146] and glare and occlusion from eyewear [117], [118], [143]. Individual differences across drivers can also diminish the accuracy of drowsiness detection.…”

Section: Drowsiness Detectionmentioning

confidence: 99%

“…For example, specific signals like blink patterns are subject to considerable individual variations [141] and difficult to detect when participants have smaller eyes [148]. Vehicle vibration and variability of driver positions with respect to cameras further exacerbate these problems [144]. Furthermore, some drivers do not show visible signs of drowsiness even when fatigued [130].…”

Section: Drowsiness Detectionmentioning

confidence: 99%

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Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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Driving safety has been a concern since the first cars appeared on the streets. Driver inattention has been singled out as a major cause of accidents early on. This is hardly surprising, as drivers routinely perform other tasks in addition to controlling the vehicle. Decades of research into what causes lapses or misdirection of drivers' attention resulted in improvements in road safety through better design of infrastructure, driver training programs, in-vehicle interfaces, and, more recently, the development of driving assistance systems (ADAS) and driving automation. This review focuses on the methods for modeling and detecting spatio-temporal aspects of drivers' attention, i.e. where and when they look, for the two latter categories of applications.We start with a brief theoretical background on human visual attention, methods for recording and measuring attention in the driving context, types of driver inattention, and factors causing it. We then discuss machine learning approaches for 1) modeling gaze for assistive and self-driving applications and 2) detecting gaze for driver monitoring. Following the overview of state-of-the-art models, we provide an extensive list of publicly available datasets that feature recordings of drivers' gaze and other attention-related annotations. We conclude with a general overview of the remaining challenges, such as data availability and quality, evaluation methods, and the limited scope of attention modeling, and outline steps toward rectifying some of these issues. Categorized and annotated lists of the reviewed models and datasets are available at https://github.com/ykotseruba/attention_and_driving

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“…Computer-vision based driver monitoring systems [8] have been used to estimate a driver's state of fatigue [28], cognitive load [17] or whether the driver's eyes are off the road [67].…”

Section: Related Workmentioning

confidence: 99%

Driver Glance Classification In-the-wild: Towards Generalization Across Domains and Subjects

Banerjee¹,

Joshi²,

Turcot³

et al. 2020

Preprint

Self Cite

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Distracted drivers are dangerous drivers. Equipping advanced driver assistance systems (ADAS) with the ability to detect driver distraction can help prevent accidents and improve driver safety. In order to detect driver distraction, an ADAS must be able to monitor their visual attention. We propose a model that takes as input a patch of the driver's face along with a crop of the eye-region and classifies their glance into 6 coarse regions-of-interest (ROIs) in the vehicle. We demonstrate that an hourglass network, trained with an additional reconstruction loss, allows the model to learn stronger contextual feature representations than a traditional encoder-only classification module. To make the system robust to subject-specific variations in appearance and behavior, we design a personalized hourglass model tuned with an auxiliary input representing the driver's baseline glance behavior. Finally, we present a weakly supervised multi-domain training regimen that enables the hourglass to jointly learn representations from different domains (varying in camera type, angle), utilizing unlabeled samples and thereby reducing annotation cost.

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In-the-wild Drowsiness Detection from Facial Expressions

Cited by 23 publications

References 22 publications

Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation

Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Driver Glance Classification In-the-wild: Towards Generalization Across Domains and Subjects

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