Analysing Driver's Attention Level using Computer Vision

Bergasa, Luis M.; Buenaposada, José Miguel; Nuevo, J.; Jiménez, Pedro Gil; Baumela, Luis

doi:10.1109/itsc.2008.4732544

Cited by 22 publications

(15 citation statements)

References 20 publications

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“…Topics like occupant position and posture, driver distraction and estimation of face orientation were examined using a multi-camera system during daylight to look in and look out of the vehicle (LiLo concept). Shortly after, in 2008, Bergasa et al reported on driver inattention analysis using a dual-camera system in the visible light range [ 131 ]. Their face detector was based on the known Viola and Jones face detection algorithm [ 132 ].…”

Section: Optical Methodsmentioning

confidence: 99%

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

Leonhardt

Leicht

Teichmann

2018

Sensors

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This review provides an overview of unobtrusive monitoring techniques that could be used to monitor some of the human vital signs (i.e., heart activity, breathing activity, temperature and potentially oxygen saturation) in a car seat. It will be shown that many techniques actually measure mechanical displacement, either on the body surface and/or inside the body. However, there are also techniques like capacitive electrocardiogram or bioimpedance that reflect electrical activity or passive electrical properties or thermal properties (infrared thermography). In addition, photopleythysmographic methods depend on optical properties (like scattering and absorption) of biological tissues and—mainly—blood. As all unobtrusive sensing modalities are always fragile and at risk of being contaminated by disturbances (like motion, rapidly changing environmental conditions, triboelectricity), the scope of the paper includes a survey on redundant sensor arrangements. Finally, this review also provides an overview of automotive demonstrators for vital sign monitoring.

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Section: Optical Methodsmentioning

confidence: 99%

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

Leonhardt

Leicht

Teichmann

2018

Sensors

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“…In addition to involuntary physiological responses to stress, a driver may have several external and voluntary reactions known as physical or behavioral responses. Physical responses are used to detect driver distraction , inattention (Bergasa et al 2008), fatigue (Sahayadhas et al 2012), mental workload (Marquart et al 2015), and stress (Fernandez and Picard 2003). The SNS regulates physical responses as conscious and voluntary actions that can be observed directly through the movement of the skeleton, muscles, and tissues of the body, including limbs, fingers, toes, neck, and face, and heard through vocalization.…”

Section: Measuring Drivers' External Physical Responsesmentioning

confidence: 99%

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

et al. 2018

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Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than in many other occupational groups. An in-vehicle warning system for driver stress levels is needed to continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, driver stress can be automatically detected using multimodal measurements. This critical review investigates the state of the art of techniques and achievements for automatic driver stress level detection based on multimodal sensors and data. In this work, the most widely used data followed by frequent and highly performed selected features to detect driver stress levels are analyzed and presented. This review also discusses key methodological issues and gaps that hinder the implementation of driver stress detection systems and offers insights into future research directions. CCS-Concepts: Human-centered computing → Ubiquitous and mobile computing; General and reference → Surveys and overviews; Computing methodologies → Machine learning algorithms

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“…Furthermore, despite the technical challenges of integrating multiple cameras, Bergasa et al [130] proposed a a subspace-based tracker based on head pose estimation using two cameras. More specifically, the initialization phase was performed using the Viola and Jones algorithm [40] and a 3D model of the face was constructed and tracked.…”

Section: Visual Distractionmentioning

confidence: 99%

“…Additionally, other software-based approaches rely on “fine” information considering both head and eye orientation in order to estimate distraction [83,130,140,141]. Pohl et al [140] focused on estimating the driver’s visual distraction level using head pose and eye gaze information with the assumption that the visual distraction level is non-linear: visual distraction increased with time (the driver looked away from the road scene) but nearly instantaneously decreased (the driver re-focused on the road scene).…”

Section: Visual Distractionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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Analysing Driver's Attention Level using Computer Vision

Cited by 22 publications

References 20 publications

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

Driver Distraction Using Visual-Based Sensors and Algorithms

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