Eye Tracking for Target Acquisition in Sparse Visualizations

Contributors

Abstract

In this paper, we present a novel marker-free method for identifying screens of interest when using head-mounted eye tracking for visualization in cluttered and multi-screen environments. We offer a solution to discerning visualization entities from sparse backgrounds by incorporating edge-detection into the existing pipeline. Our system allows for both more efficient screen identification and improved accuracy over the state-of-the-art ORB algorithm.

Publications

  • F. Wang, A. J. Bradley, and C. Collins, “Eye Tracking for Target Acquisition in Sparse Visualizations,” in ACM Symposium on Eye Tracking Research and Applications, 2020.
    [Bibtex] [PDF] [DOI]
    @InProceedings{wan2020a,
      author = {Wang, Feiyang and Bradley, Adam James and Collins, Christopher},
        booktitle = {ACM Symposium on Eye Tracking Research and Applications},
        title = {Eye Tracking for Target Acquisition in Sparse Visualizations},
        year = {2020},
        isbn = {9781450371346},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3379156.3391834},
    }

Acknowledgments

This work was supported by NSERC Canada Research Chairs.

NSERC

Research

Eye Tracking for Target Acquisition in Sparse Visualizations

Guidance in the human–machine analytics process

H-Matrix: Hierarchical Matrix for Visual Analysis of Cross-Linguistic Features in Large Learner Corpora

A Visual Analytics Framework for Adversarial Text Generation

Design by Immersion: A Transdisciplinary Approach to Problem-Driven Visualizations

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ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

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DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

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