Robust Eye Gaze Estimation

Wiśniewska, Joanna; Rezaei, Mahdi; Klette, Reinhard

doi:10.1007/978-3-319-11331-9_76

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…• Fine-grained Face Recognition: Beyond classic face recognition studies, fine-grained face recognition focuses on understanding human behavior toward face perception, such as facial expression recognition [45], [46], eye gaze detection [47], [48]. In the driving context, [49], [50] explore the predictive power of driver glances.…”

Section: Automotive Applications Of Deep Learningmentioning

confidence: 99%

MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation

et al. 2019

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Today, and possibly for a long time to come, the full driving task is too complex an activity to be fully formalized as a sensing-acting robotics system that can be explicitly solved through model-based and learning-based approaches in order to achieve full unconstrained vehicle autonomy. Localization, mapping, scene perception, vehicle control, trajectory optimization, and higher-level planning decisions associated with autonomous vehicle development remain full of open challenges. This is especially true for unconstrained, real-world operation where the margin of allowable error is extremely small and the number of edge-cases is extremely large. Until these problems are solved, human beings will remain an integral part of the driving task, monitoring the AI system as it performs anywhere from just over 0% to just under 100% of the driving. The governing objectives of the MIT Advanced Vehicle Technology (MIT-AVT) study are to (1) undertake large-scale real-world driving data collection that includes high-definition video to fuel the development of deep learning based internal and external perception systems, (2) gain a holistic understanding of how human beings interact with vehicle automation technology by integrating video data with vehicle state data, driver characteristics, mental models, and self-reported experiences with technology, and (3) identify how technology and other factors related to automation adoption and use can be improved in ways that save lives. In pursuing these objectives, we have instrumented 23 Tesla Model S and Model X vehicles, 2 Volvo S90 vehicles, 2 Range Rover Evoque, and 2 Cadillac CT6 vehicles for both long-term (over a year per driver) and medium term (one month per driver) naturalistic driving data collection. Furthermore, we are continually developing new methods for analysis of the massive-scale dataset collected from the instrumented vehicle fleet. The recorded data streams include IMU, GPS, CAN messages, and high-definition video streams of the driver face, the driver cabin, the forward roadway, and the instrument cluster (on select vehicles). The study is on-going and growing. To date, we have 122 participants, 15,610 days of participation, 511,638 miles, and 7.1 billion video frames. This paper presents the design of the study, the data collection hardware, the processing of the data, and the computer vision algorithms currently being used to extract actionable knowledge from the data. 01 231 4523 67 89 8 %& 'ÿ )*+ ,-,.,*/ÿ 0123 45 1 '142-,5 ,67ÿ 8+ *97 :; <=>ÿ @AB; CDÿ ; AE =F; GH ÿIJ KFL; M NM OFB; ÿ =F>DH ÿPQR SPT ULM VGLDH ÿPWW XGCM NY GDH ÿWZ [M Y GDÿ =LM VGBH ÿQPPR SI\ XM =GAÿ ] LF@GDH ÿJPPÿa b b a cd :; <=>ÿ =F; Fÿ NAY Y GN; M ABÿ M Dÿ ABeAM Bef ÿ :; F; M D; M NDÿ

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Section: Automotive Applications Of Deep Learningmentioning

confidence: 99%

MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation

et al. 2019

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“…Deep learning [14,19,25] can be used to analyze and process input data received from the automobile sensors such as cameras, motion sensors, laser light, GPS, Odometry, LiDAR, and radar sensors and control the vehicle in response to information from various sensors [4,10,12,13,28]. Also, we can utilize computer vision for eye-gaze tracking [10,13,25,28], monitoring threshold blinking [22] and head movement [24] which in turn such in-car sensing technologies would enable us to warn the driver of drowsiness or distraction in real-time. Hence, these measures can be highly effective in avoiding collisions and reducing fatal accidents.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Safety System for Human-Centered Semi-autonomous Vehicles

Khojasteh¹,

Alipour²,

Ansari³

et al. 2020

Data Science: From Research to Application

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Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encoder-decoder networks and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions/situations.

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“…Using computer vision technology of the existing approaches systems for extracting characteristics of a driver is very successful though but it is a challenging issue due to a variety of factors. External illumination interference, lighting conditions and the variety of eyes moving speed is the main reason [11].…”

Section: Introductionmentioning

confidence: 99%

A Study on Tiredness Measurement using Computer Vision

Hussein

Noman

Ahad

2019

JIIAE

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In this paper, we studied tiredness measurement based on several different detection methods in real time. We know that the driver tiredness is one of the major causes of traffic accidents. So tiredness detection can play a vital role for preventing road accidents. By developing an automatic solution for alerting drivers of tiredness before an accident occurs, this could reduce the number of traffic accidents. The Haar-cascade classifier is exploited based on Haar-like features to find the eyes. The main purpose of the Haar-cascade classifier is to classify closed or open state of the eyes. If we can notice that the eyes are closed for a predefined span of time, we consider the state of the eyes can be closed. Based on this closed-state of the eye, a notification (like alarm) is initiated to alert. We have detected only right eye for saving processing load on the system. The reason is that when a person closes his eyes he usually does not close one eye, but both eyes at the same time. Several steps are taken into account for this system; we first capture the frame from the webcam. Then we need to detect face as well as eye. To detect blinks, we process ROI (region of image) of pupil area. Our result is found to be satisfactory.

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Robust Eye Gaze Estimation

Cited by 6 publications

References 17 publications

MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation

MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation

An Intelligent Safety System for Human-Centered Semi-autonomous Vehicles

A Study on Tiredness Measurement using Computer Vision

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