Deep Learning-Based Eye Gaze Controlled Robotic Car

Saha, Dipayan; Ferdoushi, Munia; Emrose, Md. Tanvir; Das, Subrata; Hasan, S. M. Shafiul; Khan, Asir Intisar; Shahnaz, Celia

doi:10.1109/r10-htc.2018.8629836

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…In [33] researchers proposed a framework for eye gaze detection with the use of Convolutional-Neural-Network model and claimed better accuracy results when it comes to road safety. Along with this claim, the paradigm for the possible execution of proposed algorithm, real time eye gaze controlled autonomous vehicle can be used.…”

Section: Gaze Detection Methodsmentioning

confidence: 99%

Driver Drowsiness Detection and Monitoring System (DDDMS)

Rozali,

Fadilah,

Shariff

et al. 2022

IJACSA

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The purpose of this paper is to develop a driver drowsiness and monitoring system that could act as an assistant to the driver during the driving process. The system is aimed at reducing fatal crashes caused by driver's drowsiness and distraction. For drowsiness, the system operates by analysing eye blinks and yawn frequency of the driver while for distraction, the system works based on the head pose estimation and eye tracking. The alarm will be triggered if any of these conditions occur. Main part of the implementation of this system will be using python with computer vision, while Raspberry Pi, which is uniquely designed, for the hardware platform and the speaker for alarming. In short, this driver drowsiness monitoring system can always monitor drivers so as to avoid accidents in real time.

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Section: Gaze Detection Methodsmentioning

confidence: 99%

Driver Drowsiness Detection and Monitoring System (DDDMS)

Rozali,

Fadilah,

Shariff

et al. 2022

IJACSA

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“…context of robotic cars, Saha et al (2018) proposed a CNN architecture that estimates the direction of vision from detected eyes and surpasses the latest results from the Eye-Chimera database.…”

Section: Aiet To Improve the Process Of Visual Trackingmentioning

confidence: 99%

Eye Tracking for STEM Education Research: New Perspectives

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…In the article [2], the authors propose a neural network method for classifying the gaze direction, they take the Dlib-ml detector of the face and key points of the face, and then determine the position of the eyes. After that, both eyes are fed to the neural network and it builds the probability distribution of the classes of the gaze direction from the two images.…”

Section: Gaze Direction Classification Algorithmmentioning

confidence: 99%

Operator’s Gaze Direction Recognition

Popov

Shakhuro

Konushin

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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This work is devoted to the algorithm for recognizing the direction of a operator’s gaze. The paper considers the method of classifying the direction of gaze. The algorithm proposed in this paper allows to determine the driver’s gaze direction, which helps to protect the driving process. In the proposed work, we reviewed the algorithms and methods used to recognize the human gaze in various conditions. For each given method, a list of their positive and negative qualities is indicated. Based on the results of the review, an algorithm for classifying the car driver’s gaze direction is proposed. The proposed algorithm consists of two components. The first part is responsible for the regression of the gaze direction. The second part is responsible for classifying the results of the first part. Experimental evaluation of the developed algorithm has shown that it is effective for the task of classifying the gaze direction. An important advantage of this algorithm is that it does not require repeated training of the algorithm to adapt to other scenarios of gaze direction classification.

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Deep Learning-Based Eye Gaze Controlled Robotic Car

Cited by 6 publications

References 14 publications

Driver Drowsiness Detection and Monitoring System (DDDMS)

Driver Drowsiness Detection and Monitoring System (DDDMS)

Eye Tracking for STEM Education Research: New Perspectives

Operator’s Gaze Direction Recognition

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