Automatic Gaze Analysis: A Survey of Deep Learning based Approaches

Ghosh, Shreya; Dhall, Abhinav; Hayat, Munawar; Knibbe, Jarrod; Ji, Qiang

doi:10.48550/arxiv.2108.05479

Cited by 9 publications

(10 citation statements)

References 195 publications

(388 reference statements)

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“…A thorough analysis of gaze estimation literature is mentioned in a recent survey [24]. Prior works on eye gaze estimation can be broadly classified into hand-crafted and appearance-based methods.…”

Section: A Eye Gaze Estimationmentioning

confidence: 99%

RAZE: Region Guided Self-Supervised Gaze Representation Learning

Dubey¹,

Ghosh²,

Dhall³

2022

Preprint

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Section: A Eye Gaze Estimationmentioning

confidence: 99%

RAZE: Region Guided Self-Supervised Gaze Representation Learning

Dubey¹,

Ghosh²,

Dhall³

2022

Preprint

Self Cite

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“…With the swift rise in vehicle numbers in recent decades, traffic accidents and congestion have become increasingly prevalent and pronounced [2]. Driver visual, auditory, bio-mechanical, and cognitive distractions account for more than half of all accidents [3]. However, in recent years, significant advancements have been made in the development of Automated Driving Systems (ADS) with the aim of enhancing road safety.…”

Section: Introductionmentioning

confidence: 99%

An Explainable Attention Zone Estimation for Level 3 Autonomous Driving

Yahyaabadi,

Nikan

2023

IEEE Access

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Accurately assessing the driver's situational awareness is crucial in level 3 (L 3 ) autonomous driving, where the driver is in the loop. Estimating the attention zone provides essential information about the drivers' on/off-road visual attention and determines their readiness to take over the control from the autonomous agent in complicated situations. This paper proposes a double-phase pipeline to improve the explainability and accuracy of the attention zone estimation using an intermediate gaze regression layer, where the true relationships between the input images and output zone labels are interpretable. The proposed GazeMobileNet, a lightweight deep neural network, in the first phase, achieved state-of-the-art performance in estimating the gaze vector in the MPIIGaze dataset, with MAE of 2.37 degrees. The model was used to extract the corresponding gaze vectors from the LISA V2, which is a driving dataset with the incabin attention zone labels. As LISA V2 does not contain gaze vector labels, an unsupervised clustering approach was proposed in the second phase to categorize the driver's gaze vectors and map them to the corresponding attention zones. The proposed method demonstrated improved accuracy and robustness in the zone classification task. This model achieved the accuracies of 75.67% and 83.08% for attention zone estimation under "daytime without eyeglasses" and "nighttime without eyeglasses" capture conditions, respectively. Furthermore, the proposed model surpassed the recent research on that dataset by 73.11% and 74.02% accuracies under the "daytime with eyeglasses" and "nighttime with eyeglasses" capture conditions, respectively.INDEX TERMS Level 3 autonomy, gaze estimation, GazeMobileNet, driver's attention zone, explainable clustering.

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“…Eye Gaze: There exists a large body of work on eye gaze including estimation [15], tracking [16], forecasting [17], and using eye gaze for applications such as action recognition [3] and human-robot interaction [18], [19]. We briefly cover applications that make use of eye gaze.…”

Section: Introductionmentioning

confidence: 99%

Using Eye Gaze to Forecast Human Pose in Everyday Pick and Place Actions

Razali

Demiris

2022

2022 International Conference on Robotics and Automation (ICRA)

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Collaborative robots that operate alongside humans require the ability to understand their intent and forecast their pose. Among the various indicators of intent, the eye gaze is particularly important as it signals action towards the gazed object. By observing a person's gaze, one can effectively predict the object of interest and subsequently, forecast the person's pose. We leverage this and present a method that forecasts the human pose using gaze information for everyday pick and place actions in a home environment. Our method first attends to fixations to locate the coordinates of the object of interest before inputting said coordinates to a pose forecasting network. Experiments on the MoGaze dataset show that our gaze network lowers the errors of existing pose forecasting methods and that incorporating prior in the form of textual instructions further lowers the errors by a significant amount. Furthermore, the use of eye gaze now allows a simple multilayer perceptron network to directly forecast the keypose. a

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Automatic Gaze Analysis: A Survey of Deep Learning based Approaches

Cited by 9 publications

References 195 publications

RAZE: Region Guided Self-Supervised Gaze Representation Learning

RAZE: Region Guided Self-Supervised Gaze Representation Learning

An Explainable Attention Zone Estimation for Level 3 Autonomous Driving

Using Eye Gaze to Forecast Human Pose in Everyday Pick and Place Actions

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