Real-Time Gaze Tracking with Event-Driven Eye Segmentation
Gaze tracking is increasingly becoming an essential component in Augmented and Virtual Reality. Modern gaze tracking al gorithms are heavyweight; they operate at most 5 Hz on mobile processors despite that near-eye cameras comfortably operate at a r eal-time rate ($>$ 30 Hz). This paper presents...
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| creator | Feng, Yu Goulding-Hotta, Nathan Khan, Asif Reyserhove, Hans Zhu, Yuhao |
| description | Gaze tracking is increasingly becoming an essential component in Augmented
and Virtual Reality. Modern gaze tracking al gorithms are heavyweight; they
operate at most 5 Hz on mobile processors despite that near-eye cameras
comfortably operate at a r eal-time rate ($>$ 30 Hz). This paper presents a
real-time eye tracking algorithm that, on average, operates at 30 Hz on a
mobile processor, achieves \ang{0.1}--\ang{0.5} gaze accuracies, all the while
requiring only 30K parameters, one to two orders of magn itude smaller than
state-of-the-art eye tracking algorithms. The crux of our algorithm is an
Auto~ROI mode, which continuously pr edicts the Regions of Interest (ROIs) of
near-eye images and judiciously processes only the ROIs for gaze estimation. To
that end, we introduce a novel, lightweight ROI prediction algorithm by
emulating an event camera. We discuss how a software emulation of events
enables accurate ROI prediction without requiring special hardware. The code of
our paper is available at https://github.com/horizon-research/edgaze. |
| doi_str_mv | 10.48550/arxiv.2201.07367 |
| format | Article |
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and Virtual Reality. Modern gaze tracking al gorithms are heavyweight; they
operate at most 5 Hz on mobile processors despite that near-eye cameras
comfortably operate at a r eal-time rate ($>$ 30 Hz). This paper presents a
real-time eye tracking algorithm that, on average, operates at 30 Hz on a
mobile processor, achieves \ang{0.1}--\ang{0.5} gaze accuracies, all the while
requiring only 30K parameters, one to two orders of magn itude smaller than
state-of-the-art eye tracking algorithms. The crux of our algorithm is an
Auto~ROI mode, which continuously pr edicts the Regions of Interest (ROIs) of
near-eye images and judiciously processes only the ROIs for gaze estimation. To
that end, we introduce a novel, lightweight ROI prediction algorithm by
emulating an event camera. We discuss how a software emulation of events
enables accurate ROI prediction without requiring special hardware. The code of
our paper is available at https://github.com/horizon-research/edgaze.</description><identifier>DOI: 10.48550/arxiv.2201.07367</identifier><language>eng</language><subject>Computer Science - Human-Computer Interaction</subject><creationdate>2022-01</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2201.07367$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2201.07367$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Feng, Yu</creatorcontrib><creatorcontrib>Goulding-Hotta, Nathan</creatorcontrib><creatorcontrib>Khan, Asif</creatorcontrib><creatorcontrib>Reyserhove, Hans</creatorcontrib><creatorcontrib>Zhu, Yuhao</creatorcontrib><title>Real-Time Gaze Tracking with Event-Driven Eye Segmentation</title><description>Gaze tracking is increasingly becoming an essential component in Augmented
and Virtual Reality. Modern gaze tracking al gorithms are heavyweight; they
operate at most 5 Hz on mobile processors despite that near-eye cameras
comfortably operate at a r eal-time rate ($>$ 30 Hz). This paper presents a
real-time eye tracking algorithm that, on average, operates at 30 Hz on a
mobile processor, achieves \ang{0.1}--\ang{0.5} gaze accuracies, all the while
requiring only 30K parameters, one to two orders of magn itude smaller than
state-of-the-art eye tracking algorithms. The crux of our algorithm is an
Auto~ROI mode, which continuously pr edicts the Regions of Interest (ROIs) of
near-eye images and judiciously processes only the ROIs for gaze estimation. To
that end, we introduce a novel, lightweight ROI prediction algorithm by
emulating an event camera. We discuss how a software emulation of events
enables accurate ROI prediction without requiring special hardware. The code of
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and Virtual Reality. Modern gaze tracking al gorithms are heavyweight; they
operate at most 5 Hz on mobile processors despite that near-eye cameras
comfortably operate at a r eal-time rate ($>$ 30 Hz). This paper presents a
real-time eye tracking algorithm that, on average, operates at 30 Hz on a
mobile processor, achieves \ang{0.1}--\ang{0.5} gaze accuracies, all the while
requiring only 30K parameters, one to two orders of magn itude smaller than
state-of-the-art eye tracking algorithms. The crux of our algorithm is an
Auto~ROI mode, which continuously pr edicts the Regions of Interest (ROIs) of
near-eye images and judiciously processes only the ROIs for gaze estimation. To
that end, we introduce a novel, lightweight ROI prediction algorithm by
emulating an event camera. We discuss how a software emulation of events
enables accurate ROI prediction without requiring special hardware. The code of
our paper is available at https://github.com/horizon-research/edgaze.</abstract><doi>10.48550/arxiv.2201.07367</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Human-Computer Interaction |
| title | Real-Time Gaze Tracking with Event-Driven Eye Segmentation |
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