Toward Efficient Eye Tracking in AR/VR Devices: A Near-Eye DVS-Based Processor for Real-Time Gaze Estimation

This paper presents an efficient near-eye dynamic vision sensor (DVS)-based processor for real-time eye tracking in augmented reality/virtual reality (AR/VR) devices. The processor takes advantage of the sparse event data with fine time resolution from the DVS, addressing the need for high frame-rate, low-power, and accurate eye tracking on wearable devices with extended battery life. Exploiting the inherent sparsity of event data, we propose an event-density-based region of interest (ROI) determination method that operates directly on event stream, which requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$47\times $ </tex-math></inline-formula> fewer operations than the traditional methods, effectively overcoming the latency problem caused by the heavy computational loads. To eliminate the issue of decreasing accuracy at the edges of the field of view (FoV), we customized and fine-tuned a neural network for gaze estimation, ensuring uniformly distributed sub-degree accuracy. An estimator with a streamlined output mapping strategy and an adaptive window-sliding convolution scheme is implemented for gaze estimation acceleration. The processor is designed and fabricated in UMC 40-nm LP technology with a core area of 2.52 mm2 and performs end-to-end eye tracking exclusively with the raw event stream from DVS, achieving an average accuracy of 0.91° within a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$96^{\circ } \times 64^{\circ }$ </tex-math></inline-formula> FoV. Operating at 200 MHz, it achieves a dynamic frame rate of up to 1.2 kHz and requires only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12.7~\mu $ </tex-math></inline-formula>J of energy per gaze estimation. By integrating the DVS, the processor enables real-time, low-power, and accurate eye tracking, enhancing the immersive experience on AR/VR devices and offering intuitive and seamless interactions.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2025-04-01
DOI
https://doi.org/10.1109/tcsi.2025.3553497
Citations
7
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
13.55

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Toward Efficient Eye Tracking in AR/VR Devices: A Near-Eye DVS-Based Processor for Real-Time Gaze Estimation

Ziyi Yang, Qinyu Chen, Zhuo Zou, Jinqiao Yang et al.
7 citations
IEEE Transactions on Circuits and Systems I Regular Papers
Gaze Tracking and Assistive Technology
13.55
article

Toward Efficient Eye Tracking in AR/VR Devices: A Near-Eye DVS-Based Processor for Real-Time Gaze Estimation

Ziyi Yang, Qinyu Chen, Zhuo Zou, Jinqiao Yang, Li‐Rong Zheng, Jiayu Huang, Shihang Tan
article en
7 citations

Abstract

This paper presents an efficient near-eye dynamic vision sensor (DVS)-based processor for real-time eye tracking in augmented reality/virtual reality (AR/VR) devices. The processor takes advantage of the sparse event data with fine time resolution from the DVS, addressing the need for high frame-rate, low-power, and accurate eye tracking on wearable devices with extended battery life. Exploiting the inherent sparsity of event data, we propose an event-density-based region of interest (ROI) determination method that operates directly on event stream, which requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$47\times $ </tex-math></inline-formula> fewer operations than the traditional methods, effectively overcoming the latency problem caused by the heavy computational loads. To eliminate the issue of decreasing accuracy at the edges of the field of view (FoV), we customized and fine-tuned a neural network for gaze estimation, ensuring uniformly distributed sub-degree accuracy. An estimator with a streamlined output mapping strategy and an adaptive window-sliding convolution scheme is implemented for gaze estimation acceleration. The processor is designed and fabricated in UMC 40-nm LP technology with a core area of 2.52 mm2 and performs end-to-end eye tracking exclusively with the raw event stream from DVS, achieving an average accuracy of 0.91° within a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$96^{\circ } \times 64^{\circ }$ </tex-math></inline-formula> FoV. Operating at 200 MHz, it achieves a dynamic frame rate of up to 1.2 kHz and requires only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12.7~\mu $ </tex-math></inline-formula>J of energy per gaze estimation. By integrating the DVS, the processor enables real-time, low-power, and accurate eye tracking, enhancing the immersive experience on AR/VR devices and offering intuitive and seamless interactions.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 72(10)
Leiden University (NL), Fudan University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Gaze Tracking and Assistive Technology
13.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.