跳至内容
  • 首页
  • 资讯
  • 资源下载
  • 行业方案
  • Job招聘
  • Paper论文
  • Patent专利
  • 映维会员
  • 导航收录
  • 合作
  • 关于
  • 微信群
  • All
  • XR
  • CV
  • CG
  • HCI
  • Video
  • Optics
  • Perception
  • Reconstruction

EyeTrAES: Fine-grained, Low-Latency Eye Tracking via Adaptive Event Slicing

编辑:广东客   |   分类:CV   |   2025年2月27日

Note: We don't have the ability to review paper

PubDate: Sep 2024

Teams:Indian Institute of Technology Kharagpur, Singapore Management University

Writers:Argha Sen, Nuwan Bandara, Ila Gokarn, Thivya Kandappu, Archan Misra

PDF:EyeTrAES: Fine-grained, Low-Latency Eye Tracking via Adaptive Event Slicing

Abstract

Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in human-computer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often struggle with poor temporal resolution and computational constraints, limiting their effectiveness in capturing rapid eye movements. To address these limitations, we propose EyeTrAES, a novel approach using neuromorphic event cameras for high-fidelity tracking of natural pupillary movement that shows significant kinematic variance. One of EyeTrAES's highlights is the use of a novel adaptive windowing/slicing algorithm that ensures just the right amount of descriptive asynchronous event data accumulation within an event frame, across a wide range of eye movement patterns. EyeTrAES then applies lightweight image processing functions over accumulated event frames from just a single eye to perform pupil segmentation and tracking. We show that these methods boost pupil tracking fidelity by 6+%, achieving IoU~=92%, while incurring at least 3x lower latency than competing pure event-based eye tracking alternatives [38]. We additionally demonstrate that the microscopic pupillary motion captured by EyeTrAES exhibits distinctive variations across individuals and can thus serve as a biometric fingerprint. For robust user authentication, we train a lightweight per-user Random Forest classifier using a novel feature vector of short-term pupillary kinematics, comprising a sliding window of pupil (location, velocity, acceleration) triples. Experimental studies with two different datasets demonstrate that the EyeTrAES-based authentication technique can simultaneously achieve high authentication accuracy (~=0.82) and low processing latency (~=12ms), and significantly outperform multiple state-of-the-art competitive baselines.

本文链接:https://paper.nweon.com/16225

您可能还喜欢...

  • A Novel Method of Motion Tracking for Virtual Reality Using Magnetic Sensors

    2021年04月08日 映维

  • 691297d28dde7ce669356178c896fa68-thumb-medium

    Immersive Neural Graphics Primitives

    2023年01月11日 映维

  • Semantic Scene Completion via Integrating Instances and Scene in-the-Loop

    2021年07月02日 映维

关注:

最新AR/VR行业分享

  • ★ 映维日报:歌尔光学亮相13款AR/VR新品, Meta上线大语言模型驱动VR NPC工具 2025年9月12日
  • ★ 安徽粮食工程职业学院166万元采购VR混合现实教学实训中心 2025年9月12日
  • ★ AsynFusion框架实现高效全身音频驱动虚拟化身动画生成 2025年9月12日
  • ★ 芬兰坦佩雷大学开发VR遥操作框架控制超人体尺度重型机械臂 2025年9月12日
  • ★ 三星相机助手升级支持Galaxy XR头显空间内容创作 2025年9月12日

最新AR/VR专利

  • ★ [RSS ERROR] 无法解析RSS 2025年9月12日

最新AR/VR行业招聘

  • ★ Microsoft AR/VR Job | High Performance Compute, Director 2025年6月5日
  • ★ Microsoft AR/VR Job | Data Center Technician/ Technicien de Centre de Données 2025年6月3日
  • ★ Microsoft AR/VR Job | Senior Product Designer 2025年5月16日
  • ★ Apple AR/VR Job | AirPlay Audio Engineer 2025年3月27日
  • ★ Apple AR/VR Job | iOS Perception Engineer 2025年3月27日
  • 首页
  • 资讯
  • 资源下载
  • 行业方案
  • Job招聘
  • Paper论文
  • Patent专利
  • 映维会员
  • 导航收录
  • 合作
  • 关于
  • 微信群

联系微信:ovalics

版权所有:广州映维网络有限公司 © 2025

备案许可:粤ICP备17113731号-2

备案粤公网安备:44011302004835号

友情链接: AR/VR行业导航

读者QQ群:251118691

Quest QQ群:526200310

开发者QQ群:688769630

Paper