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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.
Resources
Publications
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![[IMG]](https://vialab.ca/wp-content/papercite-data/img/wan2020a.png)
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]](https://vialab.ca/wp-content/plugins/papercite/img/external.png)
@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.
