Look at the Driver, Look at the Road: No Distraction! No Accident!

Rezaei, Mahdi; Klette, Reinhard

doi:10.1109/cvpr.2014.24

Cited by 89 publications

(46 citation statements)

References 21 publications

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“…The driver does not need to pay attention only to driving; eye gaze or head pose (20) should also correspond (for some time) to those outside regions where safetyrelated events occur. Here, head pose or face detection is then typically followed by eye detection and an analysis of the state of the eyes or eye gaze detection.…”

Section: Driver Monitoringmentioning

confidence: 99%

“…The obvious next step is to correlate driver understanding with traffic scene understanding, for example, for warning about a detected issue for which it appears that the driver did not yet pay attention (20,69). For example, the virtual windshield (i.e., a head-up display) appears to be a good implementation for such a warning.…”

Section: Driver-environment Understandingmentioning

confidence: 99%

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Vision‐Based Driver Assistance

Klette

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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Vision‐based driver assistance systems (VB‐DAS) still assume a driver in the ego‐vehicle (i.e., that vehicle where the system operates in); autonomous driving can benefit from solutions designed for VB‐DAS. The article reviews research subjects for VB‐DAS and provides an extensive bibliography. It starts with defining basic concepts and notations in the field, also highlighting the importance of performance evaluation for VB‐DAS solutions. The article then reviews briefly safety and comfort functionalities (avoidance of blind spots, night vision, and virtual windshield), before discussing in detail solutions for basic and midlevel environment perception. This involves distance and motion computation, ego‐motion understanding, obstacle detection, tasks of object tracking (vehicles or pedestrians), and the detection and recognition of infrastructure key elements (road, lane, traffic signs, and free space). Representative VB‐DAS examples (e.g., driver monitoring and driver‐environment understanding, speed adaptation, queuing, parking, blind spot supervision, lane departure warning, wrong lane detection, intelligent headlamp control, or intercar communication) are also described. Outlook toward autonomous driving and road‐environment modeling conclude the article.

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Section: Driver Monitoringmentioning

confidence: 99%

Section: Driver-environment Understandingmentioning

confidence: 99%

Vision‐Based Driver Assistance

Klette

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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“…Recently, Rezaei and Klette [37] introduced a new algorithm for distracted driving detection using an improved 3D head pose estimation and Fermat-point transform. All the described approaches reported to work in real time.…”

Section: Previous Workmentioning

confidence: 99%

Driver Gaze Tracking and Eyes Off the Road Detection System

Vicente

Huang

Xiong

et al. 2015

IEEE Trans. Intell. Transport. Syst.

301

140

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Distracted driving is one of the main causes of vehicle collisions in the United States. Passively monitoring a driver's activities constitutes the basis of an automobile safety system that can potentially reduce the number of accidents by estimating the driver's focus of attention. This paper proposes an inexpensive vision-based system to accurately detect Eyes Off the Road (EOR). The system has three main components: 1) robust facial feature tracking; 2) head pose and gaze estimation; and 3) 3-D geometric reasoning to detect EOR. From the video stream of a camera installed on the steering wheel column, our system tracks facial features from the driver's face. Using the tracked landmarks and a 3-D face model, the system computes head pose and gaze direction. The head pose estimation algorithm is robust to nonrigid face deformations due to changes in expressions. Finally, using a 3-D geometric analysis, the system reliably detects EOR.The proposed system does not require any driver-dependent calibration or manual initialization and works in real time (25 FPS), during the day and night. To validate the performance of the system in a real car environment, we conducted a comprehensive experimental evaluation under a wide variety illumination conditions, facial expressions, and individuals. Our system achieved above 90% EOR accuracy for all tested scenarios.Index Terms-Driver monitoring system, eyes off the road detection, gaze estimation, head pose estimation.

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“…Rezaei et al [71] used a methodology to enhance the accuracy, performance and effectiveness of Haar-like classifiers, especially for complicated lighting conditions. These authors also proposed ASSAM [87], which is based on the asymmetric properties of the driver’s face due to illumination variations. A good solution is also to use a “divide and conquer” strategy to handle different variations at different stages [72].…”

Section: Face and Facial Landmarks Detectionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

101

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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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Look at the Driver, Look at the Road: No Distraction! No Accident!

Cited by 89 publications

References 21 publications

Vision‐Based Driver Assistance

Vision‐Based Driver Assistance

Driver Gaze Tracking and Eyes Off the Road Detection System

Driver Distraction Using Visual-Based Sensors and Algorithms

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