A Benchmark of Four Methods for Generating 360∘ Saliency Maps from Eye Tracking Data

2019 ◽  
Vol 13 (03) ◽  
pp. 329-341 ◽  
Author(s):  
Brendan John ◽  
Pallavi Raiturkar ◽  
Olivier Le Meur ◽  
Eakta Jain
Keyword(s):  
Virtual Reality ◽  
Eye Tracking ◽  
Video Compression ◽  
Comprehensive Analysis ◽  
Tracking Data ◽  
Saliency Maps ◽  
User Attention ◽  
Gaze Prediction

Modeling and visualization of user attention in Virtual Reality (VR) is important for many applications, such as gaze prediction, robotics, retargeting, video compression, and rendering. Several methods have been proposed to model eye tracking data as saliency maps. We benchmark the performance of four such methods for 360∘ images. We provide a comprehensive analysis and implementations of these methods to assist researchers and practitioners. Finally, we make recommendations based on our benchmark analyses and the ease of implementation.

scholarly journals Classifying Eye-Tracking Data Using Saliency Maps

2021 ◽  
Author(s):  
Shafin Rahman ◽  
Sejuti Rahman ◽  
Omar Shahid ◽  
Md. Tahmeed Abdullah ◽  
Jubair Ahmed Sourov
Keyword(s):  
Eye Tracking ◽  
Tracking Data ◽  
Saliency Maps

Eye-tracking Data Analysis for Visual Exploration Assessment and Decision Making Interpretation in Virtual Reality Environments

2020 ◽  
Author(s):  
Abner Cardoso da Silva ◽  
Cesar A. Sierra-Franco ◽  
Greis Francy M. Silva-Calpa ◽  
Felipe Carvalho ◽  
Alberto Barbosa Raposo
Keyword(s):  
Decision Making ◽  
Virtual Reality ◽  
Data Analysis ◽  
Eye Tracking ◽  
Visual Exploration ◽  
Tracking Data

scholarly journals Subtitling Virtual Reality into Arabic: Eye Tracking 360-Degree Video for Exploring Viewing Experience

2019 ◽  
Vol 64 (2) ◽  
pp. 286-308
Author(s):  
El Mehdi Ibourk ◽  
Amer Al-Adwan
Keyword(s):  
Virtual Reality ◽  
Eye Tracking ◽  
Electronic Devices ◽  
The Other ◽  
Tracking Data ◽  
Novel Technology ◽  
The Impact ◽  
Tracking Technology ◽  
Viewing Experience

Abstract The recent years have witnessed the emergence of new approaches in filmmaking including virtual reality (VR), which is meant to achieve an immersive viewing experience through advanced electronic devices, such as VR headsets. The VR industry is oriented toward developing content mainly in English and Japanese, leaving vast audiences unable to understand the original content or even enjoy this novel technology due to language barriers. This paper examines the impact of the subtitles on the viewing experience and behaviour of eight Arab participants in understanding the content in Arabic through eye tracking technology. It also provides an insight on the mechanism of watching a VR 360-degree documentary and the factors that lead viewers to favour one subtitling mode over the other in the spherical environment. For this end, a case study was designed to produce 120-degree subtitles and Follow Head Immediately subtitles, followed by the projection of the subtitled documentary through an eye tracking VR headset. The analysis of the eye tracking data is combined with post-viewing interviews in order to better understand the viewing experience of the Arab audience, their cognitive reception and the reasons leading to favour one type of subtitles over the other.

scholarly journals Predicting Video Saliency Using Crowdsourced Mouse-Tracking Data

2019 ◽  
Author(s):  
Виталий Людвиченко ◽  
Vitaliy Lyudvichenko ◽  
Дмитрий Ватолин ◽  
Dmitriy Vatolin
Keyword(s):  
Eye Tracking ◽  
Large Scale ◽  
Peripheral Vision ◽  
Tracking Data ◽  
Eye Tracker ◽  
Mouse Tracking ◽  
Saliency Maps ◽  
Video Viewing ◽  
Computer Mouse ◽  
Neural Network Algorithm

This paper presents a new way of getting high-quality saliency maps for video, using a cheaper alternative to eye-tracking data. We designed a mouse-contingent video viewing system which simulates the viewers’ peripheral vision based on the position of the mouse cursor. The system enables the use of mouse-tracking data recorded from an ordinary computer mouse as an alternative to real gaze fixations recorded by a more expensive eye-tracker. We developed a crowdsourcing system that enables the collection of such mouse-tracking data at large scale. Using the collected mouse-tracking data we showed that it can serve as an approximation of eye-tracking data. Moreover, trying to increase the efficiency of collected mouse-tracking data we proposed a novel deep neural network algorithm that improves the quality of mouse-tracking saliency maps.

scholarly journals Product Recommendation Based on Eye Tracking Data Using Fixation Duration

2021 ◽  
Vol 5 (4) ◽  
pp. 109
Author(s):  
Juni Nurma Sari ◽  
Lukito Edi Nugroho ◽  
Paulus Insap Santosa ◽  
Ridi Ferdiana
Keyword(s):  
Eye Tracking ◽  
Collaborative Filtering ◽  
Fixation Duration ◽  
Product Information ◽  
Sensor Technology ◽  
Tracking Data ◽  
Eye Tracker ◽  
Product Recommendation ◽  
User Attention ◽  
Product Recommendations

E-commerce can be used to increase companies or sellers’ profits. For consumers, e-commerce can help them shop faster. The weakness of e-commerce is that there is too much product information presented in the catalog which in turn makes consumers confused. The solution is by providing product recommendations. As the development of sensor technology, eye tracker can capture user attention when shopping. The user attention was used as data of consumer interest in the product in the form of fixation duration following the Bojko taxonomy. The fixation duration data was processed into product purchase prediction data to know consumers’ desire to buy the products by using Chandon method. Both data could be used as variables to make product recommendations based on eye tracking data. The implementation of the product recommendations based on eye tracking data was an eye tracking experiment at selvahouse.com which sells hijab and women modest wear. The result was a list of products that have similarities to other products. The product recommendation method used was item-to-item collaborative filtering. The novelty of this research is the use of eye tracking data, namely the fixation duration and product purchase prediction data as variables for product recommendations. Product recommendation that produced by eye tracking data can be solution of product recommendation’s problems, namely sparsity and cold start.

scholarly journals iMap4D: an Open Source Toolbox for Statistical Fixation Mapping of Eye-Tracking Data in Virtual Reality

2019 ◽  
Vol 19 (10) ◽  
pp. 127c
Author(s):  
Valentina Ticcinelli ◽  
Peter De Lissa ◽  
Denis Lalanne ◽  
Sebastien Miellet ◽  
Roberto Caldara
Keyword(s):  
Virtual Reality ◽  
Eye Tracking ◽  
Open Source ◽  
Tracking Data

Eye movements in sports research and practice: Immersive technologies as optimal environments for the study of gaze behaviour

2020 ◽  
Author(s):  
David Harris ◽  
Mark Wilson ◽  
Tim Holmes ◽  
Toby de Burgh ◽  
Samuel James Vine
Keyword(s):  
Virtual Reality ◽  
Eye Movements ◽  
Eye Tracking ◽  
Visual Information ◽  
Successful Performance ◽  
Experimental Control ◽  
Research And Practice ◽  
Gaze Behaviour ◽  
Immersive Technologies

Head-mounted eye tracking has been fundamental for developing an understanding of sporting expertise, as the way in which performers sample visual information from the environment is a major determinant of successful performance. There is, however, a long running tension between the desire to study realistic, in-situ gaze behaviour and the difficulties of acquiring accurate ocular measurements in dynamic and fast-moving sporting tasks. Here, we describe how immersive technologies, such as virtual reality, offer an increasingly compelling approach for conducting eye movement research in sport. The possibility of studying gaze behaviour in representative and realistic environments, but with high levels of experimental control, could enable significant strides forward for eye tracking in sport and improve understanding of how eye movements underpin sporting skills. By providing a rationale for virtual reality as an optimal environment for eye tracking research, as well as outlining practical considerations related to hardware, software and data analysis, we hope to guide researchers and practitioners in the use of this approach.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

Attentional Bias to Untrustworthy Faces: Evidence From Eye Tracking Data

2015 ◽  
Vol 23 (9) ◽  
pp. 1508
Author(s):  
Qiandong WANG ◽  
Qinggong LI ◽  
Kaikai CHEN ◽  
Genyue FU
Keyword(s):  
Eye Tracking ◽  
Attentional Bias ◽  
Tracking Data
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