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眼动跟踪研究进展与展望

苟超 卓莹 王康 王飞跃

苟超, 卓莹, 王康, 王飞跃. 眼动跟踪研究进展与展望. 自动化学报, 2022, 48(5): 1173−1192 doi: 10.16383/j.aas.c210514
引用本文: 苟超, 卓莹, 王康, 王飞跃. 眼动跟踪研究进展与展望. 自动化学报, 2022, 48(5): 1173−1192 doi: 10.16383/j.aas.c210514
Gou Chao, Zhuo Ying, Wang Kang, Wang Fei-Yue. Research advances and prospects of eye tracking. Acta Automatica Sinica, 2022, 48(5): 1173−1192 doi: 10.16383/j.aas.c210514
Citation: Gou Chao, Zhuo Ying, Wang Kang, Wang Fei-Yue. Research advances and prospects of eye tracking. Acta Automatica Sinica, 2022, 48(5): 1173−1192 doi: 10.16383/j.aas.c210514

眼动跟踪研究进展与展望

doi: 10.16383/j.aas.c210514
基金项目: 国家自然科学基金(61806198), 广州市重点研发计划(202007050002), 深圳科技计划项目(RCBS20200714114920272)资助
详细信息
    作者简介:

    苟超:中山大学智能工程学院副教授. 中国科学院大学与美国伦斯勒理工学院联合培养博士. 主要研究方向为计算机视觉和机器学习. 本文通信作者. E-mail: gouchao@mail.sysu.edu

    卓莹:中山大学智能工程学院硕士研究生. 2019年获西南交通大学交通运输与物流学院学士学位. 主要研究方向为视线估计. E-mail: zhuoy8@mail2.sysu.edu.cn

    王康:英伟达高级算法工程师. 2019年获美国伦斯勒理工学院电子信息与计算专业博士学位. 主要研究方向为自动驾驶相关的视觉算法. E-mail: kangwang.kw@gmail.com

    王飞跃:中国科学院自动化研究所研究员, 复杂系统管理与控制国家重点实验室主任, 中国科学院大学中国经济与社会安全研究中心主任, 青岛智能产业技术研究院院长. 主要研究方向为平行系统的方法与应用, 社会计算, 平行智能以及知识自动化.E-mail: feiyue.wang@ia.ac.cn

Research Advances and Prospects of Eye Tracking

Funds: Supported by National Natural Science Foundation of China (61806198), Key Research and Development Program of Guangzhou (202007050002), Shenzhen Science and Technology Program (RCBS20200714114920272)
More Information
    Author Bio:

    GOU Chao Associate professor at the School of Intelligent Engineering, Sun Yat-sen University. Ph.D. at University of Chinese Academy of Sciences, and jointly trained by Rensselaer Polytechnic Institute. His research interest covers computer vision and machine learning. Corresponding author of this paper

    ZHUO Ying Master student at the School of Intelligent Engineering, Sun Yat-sen University. She received her bachelor degree from the School of Transportation and Logistics, Southwest Jiaotong University in 2019. Her main research interest is gaze estimation

    WANG Kang Senior software engineer at Nvidia corporation. He received his Ph.D. degree in electrical, computer & system engineering from Rensselaer Polytechnic Institute in 2019. His research interest covers computer vision algorithms for autonomous cars

    WANG Fei-Yue Professor at Institute of Automation, Chinese Academy of Sciences, director of the State Key Laboratory for Management and Control of Complex Systems. Director of China Economic and Social Security Research Center at University of Chinese Acade. President of Qingdao Academy of Intelligent Industries. His research interest covers methods and applications for parallel systems, social computing, parallel intelligence, and knowledge automation

  • 摘要: 眼动跟踪是指自动检测瞳孔中心位置或者识别三维视线方向及注视点的过程, 被广泛应用于人机交互、智能驾驶、人因工程等. 由于不同场景下的光照变化、个体眼球生理构造差异、遮挡、头部姿态多样等原因, 眼动跟踪的研究目前仍然是一个具有挑战性的热点问题. 针对眼动跟踪领域,首先概述眼动跟踪研究内容, 然后分别论述近年来瞳孔中心检测及视线估计领域的国内外研究进展, 综述目前眼动跟踪主要数据集、评价指标及研究成果, 接着介绍眼动跟踪在人机交互、智能驾驶等领域的应用, 最后对眼动跟踪领域的未来发展趋势进行展望.
  • 图  1  眼动跟踪人任务及应用示例

    Fig.  1  Examples of eye tracking and corresponding applications

    图  2  苏黎世联邦理工学院的电流记录法眼动仪[17]

    Fig.  2  An eye tracker based on electrooculography from eidgenössische technische hochschule[17]

    图  3  基于 IrisParseNet 的瞳孔检测与虹膜分割结果示例图[27]

    Fig.  3  Some localization and segmentation results based on IrisParseNet[27]

    图  4  基于 FCN 的瞳孔检测结果示例图[34]

    Fig.  4  Some pupil localization results based on FCN[34]

    图  5  基于级联回归的瞳孔检测及状态估计流程[39]

    Fig.  5  The framework of cascade regression for simultaneous pupil detection and eye state estimation[39]

    图  6  基于平行视觉的瞳孔检测方法[41]

    Fig.  6  The framework of pupil detection based on parallel vision[41]

    图  7  三维眼球模型及视线估计[50]

    Fig.  7  3D eyeball model and gaze estimation[50]

    图  8  交比法示意图[57]

    Fig.  8  Gaze estimation based on cross-ratio[57]

    图  9  虚拟切平面示意图[57]

    Fig.  9  Virtual tangent plane[57]

    图  10  单应性归一法示意图[5]

    Fig.  10  Gaze estimation based on homography[5]

    图  11  基于二维关键点及三维眼球模型的视线估计[79]

    Fig.  11  Gaze estimation based on 2D landmarks and 3D eyeball model[79]

    图  12  根据用户眼动自动调整画面的智能展板[18]

    Fig.  12  A smart public display using user's eye movement to adjust the content[18]

    图  13  基于注视点的驾驶注意力分析[140]

    Fig.  13  Driving attention analysis based on the gaze points[140]

    表  1  常用瞳孔中心检测数据集

    Table  1  Datasets for pupil detection

    数据集发布年份被试人数图片/视频数量图像区域图像分辨率 (像素)
    BioID[105]200123图片 1521 张上半身384 × 280
    CASIA-Iris[107]2010≥ 1800图片 54601 张人眼、人脸320 × 280, 640 × 480, 2352 × 1728
    GI4E[106]2013103图片 1236 张上半身800 × 600
    ExCuSe[108]2015未知图片 39001 张人眼384 × 288, 620 × 460
    Else[14]2016未知图片 55712 张人眼384 × 288
    LPW[109]201622视频 66 段人眼640 × 480
    OpenEDS[110]2019152图片 356649 张人眼400 × 640
    TEyeD[111]2021132图片 20867073 张人眼384 × 288, 320 × 240, 640 × 480, 640 × 360
    下载: 导出CSV

    表  2  不同方法在BioID 数据集上的瞳孔中心检测结果对比

    Table  2  Comparison of pupil center detection results by different methods on the BioID dataset

    方法$ {d}_{eye}\le 0.05 $的
    检测准确率 (%)
    $ {d}_{eye}\le 0.10 $ 的
    检测准确率 (%)
    年份
    Ahuja等[112]92.198.02016
    Gou等[39]91.299.42017
    Choi等[113]91.198.42017
    Cai等[114]92.82018
    Levinshtein等[115]95.399.52018
    Choi等[48]93.396.92019
    Gou等[41]92.399.12019
    Xia等[34]94.499.92019
    Lee等[49]96.799.02020
    下载: 导出CSV

    表  3  不同方法在GI4E 数据集上的瞳孔中心检测结果对比

    Table  3  Comparison of pupil center detection results by different methods on the GI4E dataset

    方法${d}_{eye}\le 0.05\; 的$
    检测准确率 (%)
    ${d}_{eye}\le 0.10 \;的$
    检测准确率 (%)
    ${d}_{eye}\le 0.25 \;的$
    检测准确率 (%)
    年份
    Villanueva等[116]93.997.398.02013
    Gou等[39]94.299.199.82017
    Cai等[114]99.52018
    Levinshtein等[115]99.099.91002018
    Gou等[41]98.399.82019
    Xia等[34]99.11001002019
    Lee等[49]99.899.81002020
    Hsu等[35]97.699.61002021
    下载: 导出CSV

    表  4  常用视线估计估计数据集

    Table  4  Datasets for gaze estimation

    数据集人数图片/视频数量图像区域图像分辨率 (像素)视线角度范围 (偏航角, 俯仰角)头部姿态范围 (偏航角, 俯仰角)
    ColumbiaGaze[106]56图片 5880 张全脸5184 × 3456±15°, ±10°±30°, 0°
    EYEDIAP[117]16视频 94 段全脸640 × 480±40°, ±30°±40°, ±40°
    UT-multiview[93]50图片 64000 张全脸1280 × 1024±50°, ±36°±36°, ±36°
    GazeCapture[104]1474图片 2445504 张全脸640 × 480±18°, −1.5 ~ +20°±30°, ±40°
    MPIIGaze[118]15图片 213659 张全脸未知±20°, ±20°±25°, −10° ~ +30°
    RT-GENE[119]15图片 122531 张全脸1920 × 1080±40°, ±40°±40°, ±40°
    Gaze360[120]238图片 172000 张全脸3382 × 4096±140°, −40° ~ +10°±90°, 未知
    U2Eyes[121]1000图片 5875000 张双眼3840 × 2160未知未知
    ETH-Xgaze[122]110图片 1083492 张全脸6000 × 4000±120°, ±70°±80°, ±80°
    下载: 导出CSV

    表  5  不同方法在 MPIIGaze及 EYEDIAP数据集上的视线估计绝对误差结果对比

    Table  5  Comparison of gaze estimation results by different methods on the MPIIGaze and EYEDIAP datasets

    方法MPIIGazeEYEDIAP年份
    Hierarchical Generative[126]7.5°15.2°2018
    Dilated-Net[127]4.8°5.9°2018
    RT-GENE[119]4.3°5.9°2019
    Faze[123]5.2°2020
    FAR-NET[124]4.3°5.7°2020
    CA-Net[125]4.1°5.3°2020
    下载: 导出CSV

    表  6  主要眼动仪介绍

    Table  6  Introduction to some main eye trackers

    眼动仪型号厂商类型特点
    Tobii Pro Glasses 3Tobii眼镜式搭载16个红外光源, 配备超广角摄像机, 内置陀螺仪, 具有完整的数据采集、分析、应用程序编程接口功能支持.
    EyeLink 1000 PlusSR Research遥测式具有高采样率、低噪声等特点, 允许头部自由运动, 兼容多种第三方数据处理平台, 适用于多种研究人群和场景.
    Dikablis Glasses 3Ergoneers眼镜式轻便小巧, 误差范围约0.1° ~ 0.3°, 配备高清摄像机, 配备D-Lab数据分析软件, 可自动分析感兴趣区域.
    Smart Eye ProSmart Eye遥测式可以配置多个摄像头, 自动捕捉面部关键点, 支持视线3D重建, 配备应用程序编程接口与多种第三方数据分析软件.
    GP3Gazepoint遥测式误差范围能达到0.5° ~ 1°, 提供开放的标准应用程序编程接口和软件开发工具包, 兼容iMotions的眼动追踪模组.
    LooxidVRLooxid Labs虚拟现实可同步采集眼动和瞳孔数据. 支持脑电数据的采集, 配备数据可视化平台, 基于Unity引擎的应用程序编程接口支持定制用户交互界面和特效.
    VIVE Pro EyeHTC和Valve虚拟现实可采集眼动数据, 支持可视化. 整套系统融合了顶级的图像、音频、人体工程学硬件设计, 能营造更为真实的虚拟现实体验.
    下载: 导出CSV
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  • 收稿日期:  2021-06-08
  • 录用日期:  2021-09-17
  • 网络出版日期:  2021-10-26
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