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Bayesian online clustering of eye movement data

Published: 28 March 2012 Publication History

Abstract

The task of automatically tracking the visual attention in dynamic visual scenes is highly challenging. To approach it, we propose a Bayesian online learning algorithm. As the visual scene changes and new objects appear, based on a mixture model, the algorithm can identify and tell visual saccades (transitions) from visual fixation clusters (regions of interest). The approach is evaluated on real-world data, collected from eye-tracking experiments in driving sessions.

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Camilli, M., Nacchia, R., Terenzi, M., and Nocera, F. D. 2008. Astef: A simple tool for examining fixations. Behavior Research Methods 40, 373--382.
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  • (2024)Gaze Supervision for Mitigating Causal Confusion in Driving Agents2024 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55156.2024.10588498(2331-2338)Online publication date: 2-Jun-2024
  • (2024)Optimising virtual object position for efficient eye-gaze interaction in Hololens2Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization10.1080/21681163.2024.233776512:1Online publication date: 14-Apr-2024
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cover image ACM Conferences
ETRA '12: Proceedings of the Symposium on Eye Tracking Research and Applications
March 2012
420 pages
ISBN:9781450312219
DOI:10.1145/2168556
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 March 2012

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Author Tags

  1. Bayesian model
  2. eye movement data
  3. fixation clusters
  4. online clustering

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ETRA '12
ETRA '12: Eye Tracking Research and Applications
March 28 - 30, 2012
California, Santa Barbara

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Overall Acceptance Rate 69 of 137 submissions, 50%

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Cited By

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  • (2024)Gaze-Based Intention Estimation: Principles, Methodologies, and Applications in HRIACM Transactions on Human-Robot Interaction10.1145/365637613:3(1-30)Online publication date: 26-Sep-2024
  • (2024)Gaze Supervision for Mitigating Causal Confusion in Driving Agents2024 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55156.2024.10588498(2331-2338)Online publication date: 2-Jun-2024
  • (2024)Optimising virtual object position for efficient eye-gaze interaction in Hololens2Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization10.1080/21681163.2024.233776512:1Online publication date: 14-Apr-2024
  • (2023)Creative and Progressive Interior Color Design with Eye-tracked User PreferenceACM Transactions on Computer-Human Interaction10.1145/354292230:1(1-31)Online publication date: 7-Mar-2023
  • (2023)Eye‐Tracking‐Based Prediction of User Experience in VR Locomotion Using Machine LearningComputer Graphics Forum10.1111/cgf.1470341:7(589-599)Online publication date: 20-Mar-2023
  • (2023)Characterizing Drivers’ Peripheral Vision via the Functional Field of View for Intelligent Driving Assistance2023 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55152.2023.10186746(1-8)Online publication date: 4-Jun-2023
  • (2023)Eye Tracking in Virtual Reality: a Broad Review of Applications and ChallengesVirtual Reality10.1007/s10055-022-00738-z27:2(1481-1505)Online publication date: 18-Jan-2023
  • (2022)Online System Prognostics with Ensemble Models and Evolving ClusteringMachines10.3390/machines1101004011:1(40)Online publication date: 29-Dec-2022
  • (2022)Semantics characterization for eye shapes based on directional triangle-area curve clusteringMultimedia Tools and Applications10.1007/s11042-019-7659-478:18(25373-25406)Online publication date: 10-Mar-2022
  • (2021)MemXProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34635095:2(1-23)Online publication date: 24-Jun-2021
  • Show More Cited By

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