眼动跟踪研究进展与展望

苟超; 卓莹; 王康; 王飞跃

doi:10.16383/j.aas.c210514

眼动跟踪研究进展与展望

doi: 10.16383/j.aas.c210514

苟超^1,,
卓莹^1,,
王康^2,,
王飞跃^3,

1.
中山大学智能工程学院深圳 518107 中国
2.
英伟达圣克拉拉 95051 美国
3.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190 中国

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

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出版历程
- 收稿日期: 2021-06-08
- 录用日期: 2021-09-17
- 网络出版日期: 2021-10-26
- 刊出日期: 2022-05-13

Research Advances and Prospects of Eye Tracking

1.
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
2.
Nvidia, Santa Clara 95051, USA
3.
The State Key Laboratory for Management and Control of Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

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

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.
- 眼动跟踪 /
- 人眼检测 /
- 视线估计 /
- 注意力分析
Abstract: Eye tracking is a process of automatically detecting the location of pupil or recognizing the gaze direction and gaze point, and is widely used in human-computer interaction, intelligent driving, ergonomics, and so on. Eye tracking is still a challenging and hot topic due to the changes of illumination, the differences of individual eyeball physiological structure, occlusion, and the diversity of head pose. In this paper, we focus on the study of eye tracking. Firstly, we give an introduction of eye tracking, followed by discussing the research progress of pupil detection and gaze estimation in recent years. Then, we review the datasets and evaluation metrics with results, followed by introducing the application of eye tracking in human-computer interaction, intelligent driving and other fields. Finally, the future trends of eye tracking are discussed.
- Eye tracking /
- eye detection /
- gaze estimation /
- attention analysis

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图 1 眼动跟踪人任务及应用示例

Fig. 1 Examples of eye tracking and corresponding applications

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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]

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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]

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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]

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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]

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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]

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图 7 三维眼球模型及视线估计^[50]

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

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图 8 交比法示意图^[57]

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

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图 9 虚拟切平面示意图^[57]

Fig. 9 Virtual tangent plane^[57]

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图 10 单应性归一法示意图^[5]

Fig. 10 Gaze estimation based on homography^[5]

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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]

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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]

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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]

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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]	2001	23	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]	2013	103	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]	2016	22	视频 66 段	人眼	640 × 480
OpenEDS^[110]	2019	152	图片 356649 张	人眼	400 × 640
TEyeD^[111]	2021	132	图片 20867073 张	人眼	384 × 288, 320 × 240, 640 × 480, 640 × 360

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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]	92.1	98.0	2016
Gou等^[39]	91.2	99.4	2017
Choi等^[113]	91.1	98.4	2017
Cai等^[114]	92.8	—	2018
Levinshtein等^[115]	95.3	99.5	2018
Choi等^[48]	93.3	96.9	2019
Gou等^[41]	92.3	99.1	2019
Xia等^[34]	94.4	99.9	2019
Lee等^[49]	96.7	99.0	2020

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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]	93.9	97.3	98.0	2013
Gou等^[39]	94.2	99.1	99.8	2017
Cai等^[114]	99.5	—	—	2018
Levinshtein等^[115]	99.0	99.9	100	2018
Gou等^[41]	98.3	99.8	—	2019
Xia等^[34]	99.1	100	100	2019
Lee等^[49]	99.8	99.8	100	2020
Hsu等^[35]	97.6	99.6	100	2021

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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°

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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	MPIIGaze	EYEDIAP	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

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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 3	Tobii	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 Plus	SR 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 3	Ergoneers	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 Pro	Smart 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.
GP3	Gazepoint	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.
LooxidVR	Looxid 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 Eye	HTC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.

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