Dual-Cameras-Based Driver’s Eye Gaze Tracking System with Non-Linear Gaze Point Refinement

Ding, Xueyan; Yuan, Guoliang; Fu, Xianping

doi:10.3390/s22062326

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…This technology is being studied in fields such as education, engineering, gaming, and medicine [ 1 , 2 , 3 , 4 , 5 ]. Research has been conducted to identify or train reality-based users’ behaviors by applying it to automobiles, wheelchairs, and augmented reality [ 6 , 7 , 8 ]. However, research on eye-gaze direction-tracking is not limited to recognition and the analysis of physical changes in eye movement.…”

Section: Introductionmentioning

confidence: 99%

Improving Performance of the Human Pupil Orbit Model (HPOM) Estimation Method for Eye-Gaze Tracking

Lee

Jeong

Kim

et al. 2022

Sensors

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Eye-gaze direction-tracking technology is used in fields such as medicine, education, engineering, and gaming. Stability, accuracy, and precision of eye-gaze direction-tracking are demanded with simultaneous upgrades in response speed. In this study, a method is proposed to improve the speed with decreases in the system load and precision in the human pupil orbit model (HPOM) estimation method. The new method was proposed based on the phenomenon that the minor axis of the elliptical-deformed pupil always pointed toward the rotational center presented in various eye-gaze direction detection studies and HPOM estimation methods. Simulation experimental results confirmed that the speed was improved by at least 74 times by consuming less than 7 ms compared to the HPOM estimation. The accuracy of the eye’s ocular rotational center point showed a maximum error of approximately 0.2 pixels on the x-axis and approximately 8 pixels on the y-axis. The precision of the proposed method was 0.0 pixels when the number of estimation samples (ES) was 7 or less, which showed results consistent with those of the HPOM estimation studies. However, the proposed method was judged to work conservatively against the allowable angle error (AAE), considering that the experiment was conducted under the worst conditions and the cost used to estimate the final model. Therefore, the proposed method could estimate HPOM with high accuracy and precision through AAE adjustment according to system performance and the usage environment.

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Section: Introductionmentioning

confidence: 99%

Improving Performance of the Human Pupil Orbit Model (HPOM) Estimation Method for Eye-Gaze Tracking

Lee

Jeong

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Mizuno et al [5] developed a system that detects visual attention by utilizing a gaze tracker and a device installed in the vehicle. Moreover, some researchers implemented a driver's gaze mapping system that uses an additional camera [6], [7]. While…”

mentioning

confidence: 99%

Domain adaptation for driver's gaze mapping for different drivers and new environments

Sonom-Ochir,

Karungaru,

Terada

et al. 2024

Int. J. Adv. Intell. Informatics

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Distracted driving is a leading cause of traffic accidents, and often arises from a lack of visual attention on the road. To enhance road safety, monitoring a driver's visual attention is crucial. Appearance-based gaze estimation using deep learning and Convolutional Neural Networks (CNN) has shown promising results, but it faces challenges when applied to different drivers and environments. In this paper, we propose a domain adaptation-based solution for gaze mapping, which aims to accurately estimate a driver's gaze in diverse drivers and new environments. Our method consists of three steps: pre-processing, facial feature extraction, and gaze region classification. We explore two strategies for input feature extraction, one utilizing the full appearance of the driver and environment and the other focusing on the driver's face. Through unsupervised domain adaptation, we align the feature distributions of the source and target domains using a conditional Generative Adversarial Network (GAN). We conduct experiments on the Driver Gaze Mapping (DGM) dataset and the Columbia Cave-DB dataset to evaluate the performance of our method. The results demonstrate that our proposed method reduces the gaze mapping error, achieves better performance on different drivers and camera positions, and outperforms existing methods. We achieved an average Strictly Correct Estimation Rate (SCER) accuracy of 81.38% and 93.53% and Loosely Correct Estimation Rate (LCER) accuracy of 96.69% and 98.9% for the two strategies, respectively, indicating the effectiveness of our approach in adapting to different domains and camera positions. Our study contributes to the advancement of gaze mapping techniques and provides insights for improving driver safety in various driving scenarios.

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“…Figure 2. Eye position extraction method [4].Wang et al proposed a method for the driver to track the human eye fixation point using the nonlinear fixation point refinement of the dual camera system [9]…”

mentioning

confidence: 99%

Driver's hazardous state detection in human-computer interaction of automotive cockpits

Zhang

2024

ACE

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Today, the smart car industry is growing rapidly, the functions of the intelligent cockpit based on human-computer interaction are more and more extensive, and the sales volume of intelligent vehicles continues to rise. The incidence of traffic crashes caused by the unsafe state of drivers remains high. The different behavioral states that drivers may emit during driving is a necessary consideration in the design of the intelligent cockpit. This paper takes the driver's state as the starting point to systematically consider the driver's state detection. Summarizing the driver's state detection from four parts: eye state, limb state, facial state, and language state. This paper introduces the development status of the current four types of detection systems, focusing on eye state recognition and limb state recognition. The key driver's characteristic signals are mainly collected by the camera. The driver's state is judged by deep learning, machine learning, and database. This paper is more systematic and comprehensive than the existing literature. Comprehensive consideration of the driver's state contributes to the driver and passengers.

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Dual-Cameras-Based Driver’s Eye Gaze Tracking System with Non-Linear Gaze Point Refinement

Cited by 11 publications

References 35 publications

Improving Performance of the Human Pupil Orbit Model (HPOM) Estimation Method for Eye-Gaze Tracking

Improving Performance of the Human Pupil Orbit Model (HPOM) Estimation Method for Eye-Gaze Tracking

Domain adaptation for driver's gaze mapping for different drivers and new environments

Driver's hazardous state detection in human-computer interaction of automotive cockpits

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