Automatic Analysis of Eye-Tracking Data for Augmented Reality Applications: A Prospective Outlook

Naspetti, Simona; Pierdicca, Roberto; Mandolesi, Serena; Paolanti, Marina; Frontoni, Emanuele; Zanoli, Raffaele

doi:10.1007/978-3-319-40651-0_17

Cited by 42 publications

(22 citation statements)

References 23 publications

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“…Participants' experience of virtual museums has been measured via eye-tracking (Bastanlar, 2007) indicating that visitors preferred semantic information, 360 degree visual angles and floor map. Naspetti et al (2016) found that a viewer's attention was influenced by added augmented reality applications. Eye-tracking has been used to evaluate the aesthetics of lake sightseeing (Potocka, 2013) and the educational use of 3D…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

“…ethnic, knowledge, and country of origin) (Hernandez-Mendez & Munoz-Leiva, 2015;Pan et al, 2013;Tzuaan et al, 2014;Wang & Sparks, 2016), and purpose (e.g. natural or targeted, expect or non-expert) (Li et al, 2016;Naspetti et al, 2016;Sang, Tveit, Pihel, & Hägerhäll, 2016) affect their attention to and interaction with their environment. Further studies of cultural differences in attention appear useful area for research.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

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A review of eye-tracking research in tourism

Scott

Zhang

et al. 2017

Current Issues in Tourism

149

118

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Section: Conclusion and Future Researchmentioning

confidence: 99%

Section: Conclusion and Future Researchmentioning

confidence: 99%

A review of eye-tracking research in tourism

Scott

Zhang

et al. 2017

Current Issues in Tourism

149

118

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“…Furthermore, compared with 2D static maps, dynamic map symbols [90,91], dynamic interactive applications [92][93][94], and panoramic maps [95] have attracted much more attention in cartographers. In the future, eye tracking techniques might make great contributions to cartography in the usability research of VR (virtual reality) [96], AR (augmented reality) [97], emotional recognition [98,99], etc.…”

Section: Discussion Related To Current Trendsmentioning

confidence: 99%

Visualizing the Intellectual Structure of Eye Movement Research in Cartography

Wang

Chen

Yuan

et al. 2016

IJGI

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Abstract:Eye movement research is a burgeoning frontier area in cartography that has attracted much attention from cartographers. However, the substantial amount of relevant literature poses a challenge for researchers aiming to obtain a rapid understanding of the intellectual structure of this research field. The purpose of this paper is to introduce the use of bibliometric analysis methods and multiple visual metaphors to visualize the intellectual structure of eye movement research in cartography, including the classic literature, research theme clusters, and research hotspots, etc. We also explain the use of geovisualization method, which can efficiently represent the spatial distribution of scientific power. Although the analysis results may not fully describe the whole research field, this method is generally applicable. We hope that it will not only help researchers to quickly grasp the evolution and trends of this research field, but will also become a novel method of merging geovisualization with knowledge visualization.

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“…This provides new answers to predict the museums' visitor characteristics. The main contributions of this paper, aside from extending the system and the analysis presented in References [14,15], are (i) the proposal of a novel VAM to help identify the type of user in front of paintings; (ii) the collection and analysis of a real eye-tracking CH dataset for deep learning purposes that is public to all researchers and also useful to art researchers and artists alike (http://vrai.dii.univpm.it/content/eye-tracking-dataset-ch); (iii) an in-depth analysis to investigate whether there is a positive attentional bias when looking at similar paintings, and (iv) a real environment test with results that prove the main goals of the paper mentioned above.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we have proposed a novel Visual Attentive Model (VAM) that is learned from eye tracking data. As demonstrated by our previous work in this field [14,15], the exploitation of a user's gaze proved to be particularly useful for the development of Augmented Reality applications [16]. In fact, while the current practice for the implementation of such kinds of multimedia experience is to deliver applications from the domain expert's point of view, it has been proved that a user-centered design is more effective, with the contents tailored according to the user's preferences.…”

Section: Introductionmentioning

confidence: 99%

A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage

Pierdicca

Paolanti

Quattrini

et al. 2020

Sensors

Self Cite

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In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data. In particular, eye-tracking data of adults and children observing five paintings with similar characteristics have been collected. The images are selected by CH experts and are—the three “Ideal Cities” (Urbino, Baltimore and Berlin), the Inlaid chest in the National Gallery of Marche and Wooden panel in the “Studiolo del Duca” with Marche view. These pictures have been recognized by experts as having analogous features thus providing coherent visual stimuli. Our proposed method combines a new coordinates representation from eye sequences by using Geometric Algebra with a deep learning model for automated recognition (to identify, differentiate, or authenticate individuals) of people by the attention focus of distinctive eye movement patterns. The experiments were conducted by comparing five Deep Convolutional Neural Networks (DCNNs), yield high accuracy (more than 80 %), demonstrating the effectiveness and suitability of the proposed approach in identifying adults and children as museums’ visitors.

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Automatic Analysis of Eye-Tracking Data for Augmented Reality Applications: A Prospective Outlook

Cited by 42 publications

References 23 publications

A review of eye-tracking research in tourism

A review of eye-tracking research in tourism

Visualizing the Intellectual Structure of Eye Movement Research in Cartography

A Visual Attentive Model for Discovering Patterns in Eye-Tracking Data—A Proposal in Cultural Heritage

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