Contributors:
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.
The source code for this project is available on our Github.
Publications
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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.
@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},
}
Acknowledgements
