Driver State Monitor from DELPHI

Edenborough, N.; Hammoud, Riad I.; Harbach, A.; Ingold, Anne; Kisačanin, Branislav; Malawey, P.; Newman, Timothy S.; Scharenbroch, G.; Skiver, S.; Smith, Matthew; Wilhelm, Andrew S.; Witt, Gerald; Yoder, E.; Zhang, H.

doi:10.1109/cvpr.2005.135

Cited by 20 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In many distraction detection systems, the use of commercial sensors is usually performed [77,239,241,245,246,247]. We understand that the reason from this is twofold: these systems are well-established solutions offering both head and gaze tracking in the car environment and the efforts of the investigation can be focused to detect and predict distraction from the outputs from these commercial sensors instead of developing a new sensor from the very beginning.…”

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

“…We understand that the reason from this is twofold: these systems are well-established solutions offering both head and gaze tracking in the car environment and the efforts of the investigation can be focused to detect and predict distraction from the outputs from these commercial sensors instead of developing a new sensor from the very beginning. These commercial sensors can operate using one camera [239,245,246,247], two cameras [241] or even up to 8 cameras [77] placed all over the vehicle cabin. What we find missing is some research works trying to compare these commercial sensors in order to highlight the pros and cons of each one.…”

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

“…Delphi Electronics, a major automotive electronics manufacturer, developed a single camera Driver Status Monitor (DSM) [246]. By detecting and tracking the driver’s facial features, the system analyzes eye-closures and head pose to infer his/her fatigue or distraction.…”

Section: Sensorsmentioning

confidence: 99%

See 2 more Smart Citations

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

104

View full text Add to dashboard Cite

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.

show abstract

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

See 1 more Smart Citation

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

104

View full text Add to dashboard Cite

show abstract

“…As a result, it is unfeasible to install this system in regular cars. Other commercial systems include the ones developed by Delphi Electronics [15] and SensoMotoric Instruments GmbH [4].…”

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.

302

140

View full text Add to dashboard Cite

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.

show abstract

“…In motor vehicles, for example, a camera can be used to generate an image of the driver's face, and portions of the image corresponding to the driver's eyes can be analyzed to assess drive gaze or drowsiness [12,18,36,46,47,49]. In early stages, eye tracking has been employed by psychologists to conduct human factor experiments [9,44,58].…”

Section: Introductionmentioning

confidence: 99%