基于特征的视线跟踪方法研究综述

刘佳惠; 迟健男; 尹怡欣

doi:10.16383/j.aas.c180844

基于特征的视线跟踪方法研究综述

doi: 10.16383/j.aas.c180844

刘佳惠^1,,
迟健男^{1, 2, ,},
尹怡欣^1,

1.
北京科技大学自动化学院北京 100083
2.
北京科技大学顺德研究生院佛山 528399

基金项目:

国家重点研发计划 2018YFC2001700

北京市自然科学基金 4172040

中央高校基本科研业务费专项资金 FRF-GF-20-04A

详细信息

作者简介:
刘佳惠北京科技大学自动化学院博士研究生.主要研究方向为计算机视觉与模式识别. E-mail: ustbljh@163.com

尹怡欣北京科技大学自动化学院教授. 2002年获得北京科技大学控制理论与控制工程系博士学位.主要研究方向为复杂系统的建模与控制, 网络控制, 流程工业自动化. E-mail: yyx@ies.ustb.edu.cn

通讯作者:
迟健男北京科技大学自动化学院副教授. 2005年获得东北大学模式识别与智能系统系博士学位.主要研究方向为计算机视觉, 人机交互, 光学测量.本文通信作者. E-mail: ustbjnc@ustb.edu.cn

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出版历程
- 收稿日期: 2018-12-20
- 录用日期: 2019-04-19
- 刊出日期: 2021-02-26

A Review of Feature-based Gaze Tracking Methods

LIU Jia-Hui^1
,,
CHI Jian-Nan^{1, 2
, ,},
YIN Yi-Xin^1
,

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083
2.
Shunde Graduate School of University Of Science and Technology Beijing, Foshan 528399

Funds:

National Key R & D Program of China 2018YFC2001700

Beijing Municipal Natural Science Foundation 4172040

the Fundamental Research Funds for the Central Universities FRF-GF-20-04A

More Information

Author Bio:
LIU Jia-Hui Ph. D. candidate at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. Her research interest covers computer vision and pattern recognition

YIN Yi-Xin Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. In 2002, he received his Ph. D. degree from the Department of Control Theory and Control Engineering, University of Science and Technology Beijing. His research interest covers modeling and control of complex systems, network control, and process industry automation

Corresponding author: CHI Jian-Nan Associate professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. In 2005, he received his Ph. D. degree from the Department of Pattern Recognition and Intelligent Systems, Northeastern University. His research interest covers computer vision, human-computer interaction, and optical measurement. Corresponding author of this paper

摘要

摘要: 针对基于特征的视线跟踪方法进行了综述.首先对视线跟踪技术的发展、相关研究工作和研究现状进行了阐述; 然后将基于特征的视线跟踪方法分成了两大类:二维视线跟踪方法和三维视线跟踪方法, 从硬件系统配置、误差主要来源、头部运动影响、优缺点等多个方面重点分析了这两类视线跟踪方法, 对近五年现有的部分基于特征的视线跟踪方法进行了对比分析, 并对二维视线跟踪系统和三维视线跟踪系统中的几个关键问题进行了探讨; 此外, 介绍了视线跟踪技术在人机交互、医学、军事、智能交通等多个领域的应用; 最后对基于特征的视线跟踪方法的发展趋势和研究热点进行了总结与展望.
- 基于特征 /
- 视线跟踪 /
- 二维映射模型 /
- 三维视线估计
Abstract: In this paper, the feature-based gaze tracking methods are reviewed. Firstly, the development, the related works and the research status of gaze tracking technology are introduced. Then, the feature-based gaze tracking methods are classified into two categories: Two-dimensional gaze tracking methods and three-dimensional gaze tracking methods. These two kinds of gaze tracking methods are emphatically analyzed in terms of hardware system configuration, main error sources, head movement, advantages and disadvantages, etc. Some of the existing feature-based gaze tracking methods in recent five years are compared, and several key issues in two-dimensional gaze tracking systems and three-dimensional gaze tracking systems are discussed. Moreover, the applications of gaze tracking technology in many fields, such as human-computer interaction, medicine, military, intelligent transportation, etc. are introduced. Finally, the development trends and research topics of feature-based gaze tracking methods are summarized.
- Feature-based /
- gaze tracking /
- two-dimensional mapping model /
- three-dimensional gaze estimation
Recommended by Associate Editor HUANG Qing-Ming
注释:

1) 本文责任编委黄庆明

HTML全文

图 1 基于特征的视线跟踪方法原理流程图

Fig. 1 The flow diagram for feature-based gaze tracking methods

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图 2 瞳孔(或虹膜)-眼角法视线跟踪系统示意图

Fig. 2 The gaze tracking system of pupil (or iris)-corner technique

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图 3 "亮瞳"现象与"暗瞳"现象^[43]

Fig. 3 Bright pupil and dark pupil^[43]

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图 4 交比值法视线估计模型

Fig. 4 Cross-ratio based gaze estimation model

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图 5 单应性归一化法视线估计模型

Fig. 5 HN based gaze estimation model

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图 6 人眼模型

Fig. 6 Eye model

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图 7 光轴重建方法示意图

Fig. 7 The methods of optical axis reconstruction

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图 8 三维视线估计关键技术构成图

Fig. 8 Key technologies of three-dimensional gaze estimation

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图 9 基于Kinect的三维视线估计模型

Fig. 9 3D gaze estimation model based on a kinect

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图 10 单相机单光源系统三维视线估计模型

Fig. 10 3D gaze estimation model based on one-camera-one-light-source system

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图 11 单相机多光源系统三维视线估计模型

Fig. 11 3D gaze estimation model based on one-camera-multi-light-source system

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图 12 多相机多光源系统中角膜中心的求解

Fig. 12 Solution of the cornea center in multi-camera-multi-light-source system

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图 13 多相机多光源系统中折射平面法求解光轴

Fig. 13 Refraction plane method for solving the optical axis in multi-camera-multi-light-source system

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图 14 Kappa角示意图

Fig. 14 Diagram of kappa angle

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图 15 视线跟踪技术在人机交互中的应用

Fig. 15 Applications of gaze tracking technology in human-computer interaction

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图 16 视线跟踪技术在医学诊断中的应用

Fig. 16 Applications of gaze tracking technology in medical diagnosis

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图 17 视线跟踪技术在军事领域的应用

Fig. 17 Applications of gaze tracking technology in military

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图 18 视线跟踪技术在智能交通领域的应用

Fig. 18 Applications of gaze tracking technology in intelligent transportation

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图 19 视线跟踪技术在人因分析中的应用

Fig. 19 Applications of gaze tracking technology in human factors analysis

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图 20 视线跟踪技术在虚拟现实中的应用

Fig. 20 Applications of gaze tracking technology in virtual reality

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表 1 二维视线跟踪方法的特点对比

Table 1 Comparison of the characteristics of two-dimensional gaze tracking methods

方法	相机	光源	误差主要来源	头动影响	优点	缺点
瞳孔(虹膜)—眼角法	1	0	眼角检测头部偏离标定位置	头部固定	系统简单便于操作与实现	用户标定复杂头部固定带来的不适感
瞳孔(虹膜)—角膜反射法	1	1	头部偏离标定位置	头部固定	系统简单无需标定光源和屏幕位置	用户标定复杂头部固定带来的不适感
	1	$\geq$2	普尔钦斑检测	头部一定范围运动	无需标定光源和屏幕位置模型相对简单	用户标定复杂
	$\geq$2	$\geq$1	多个相机的转换关系	头部自由运动	允许头部自由运动精度较高	用户标定复杂、系统标定复杂相机标定复杂
交比值法	$\geq$1	$\geq$4	虚拟正切平面的构建	头部一定范围运动	免相机标定模型简单	四个光源均需成像限制头部运动范围需多点用户标定补偿光轴与视轴的夹角
HN法	1	$\geq$4	近似瞳孔在普尔钦斑构成的平面中	头部一定范围运动	无需已知光源构成的矩形尺寸模型简单补偿了光轴与视轴的夹角	四个光源均需成像限制头部运动范围用户标定复杂

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表 2 三维视线跟踪方法的特点对比

Table 2 Comparison of the characteristics of three-dimensional gaze tracking methods

方法	相机	光源	误差主要来源	头动影响	视线信息	优点	缺点
基于深度相机的三维视线估计方法	1	0	眼球中心、虹膜中心等中间量的精确计算	头部一定范围运动	视线落点	系统简单、模型简单	用户标定复杂、视线精度非常依赖于人眼特征检测的精度
基于普通相机的三维视线估计方法	1	1	预先设定的人眼不变参数	头部一定范围运动	三维视线	系统简单、便于实现	未考虑个体差异性, 精度较低
	1	$\geq$2	普尔钦斑检测、人眼不变参数的求解或预先设定	头部一定范围运动	三维视线	系统相对简单、可计算空间三维信息	解非线性方程组求取人眼不变参数, 运算速度较慢, 精度较低
	$\geq$2	$\geq$1	系统标定	头部自由运动	三维视线	用户标定简单、视线精度高	系统复杂、系统标定复杂、成本较高

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表 3 二维视线跟踪方法与三维视线跟踪方法的比较

Table 3 Comparison of two-dimensional gaze tracking methods and three-dimensional gaze tracking methods

类型	系统配置	系统标定	用户标定	视线精度	头部运动	应用场景
二维视线跟踪方法	单相机无光源系统	—	复杂	较低	$\times$	系统简单, 精度较低, 应用场合有限
	单相机单光源系统	—	复杂	较高(头部固定)	$\times$	头部固定或微小运动, 适用于穿戴式或遥测式设备, 适合人因分析、认知疾病诊断等应用
	单相机多光源系统	简单	复杂	较高	$\surd$	头部较小运动范围, 适用于穿戴式或遥测式设备, 适合心理分析、助残等应用
	多相机多光源系统	复杂	复杂	高	$\surd$	头部较大运动范围, 精度要求较高, 适合虚拟现实等应用
三维视线跟踪方法	单相机无光源系统	复杂	复杂	较低	$\surd$	需获取真实彩色图像, 精度要求低, 适用于移动便携式设备、网络终端设备
	单相机单光源系统	复杂	较复杂	较低	$\surd$	在三维空间物体上的注视点, 精度要求较低, 适用于移动便携式设备, 如手机、平板电脑等
	单相机多光源系统	复杂	较复杂	较高	$\surd$	在三维空间物体上的注视点, 精度要求一般, 适用于穿戴式或遥测式设备, 适合虚拟现实等应用
	多相机多光源系统	复杂	简单	高	$\surd$	在三维空间物体上的注视点, 精度要求较高, 适用于穿戴式或遥测式设备, 适合虚拟现实等应用

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表 4 近五年部分基于特征的视线跟踪方法对比

Table 4 Comparison of feature-based gaze tracking methods in recent five years

文献	相机	光源	类型	特征	头动	精度	特殊说明
Yu等^[37]	1	0	二维映射	虹膜、眼角	头部固定	水平0.99$^\circ $, 竖直1.33$^\circ $
Skodras等^[39]	1	0	二维映射	眼角、眼睑、脸部定位点	头部固定	1.42$^\circ $$\pm$1.35$^\circ $	免相机标定
Xia等^[40]	1	0	二维映射	眼角、瞳孔	头部固定	水平0.84$^\circ $, 竖直0.56$^\circ $	简单映射模型
Cheung等^[41]	1	0	二维映射	虹膜、眼角	运动范围: 15.5$^\circ\times15.5^\circ\times 5^\circ $	2.27$^\circ $
熊春水等^[27]	1	1	二维映射	瞳孔、普尔钦斑	运动范围: 100mm$\times$ 100mm $\times$ 100mm	1.05$^\circ $	引入增量学习, 单点标定
Blignaut等^[48]	1	1	二维映射	瞳孔、普尔钦斑	头部固定	0.5$^\circ $	标定点个数$\geq$12
Banaeeyan等^[52]	1	2	二维映射	虹膜、眼角	头部固定	水平0.928$^\circ $, 竖直1.282$^\circ $
Cheng等^[62]	1	5	二维交比值	瞳孔、普尔钦斑	自由运动	0.70$^\circ $	动态正切平面
Arar等^[63]	1	5	二维交比值	瞳孔、普尔钦斑	自由运动	1.15$^\circ $(5点标定)	图像分辨率低, 标定点少
Zhang等^[67]	1	8	二维HN	瞳孔、普尔钦斑	运动范围: 80mm$\times$ 100mm $\times$ 100mm	0.56$^\circ $	双目视觉
Ma等^[69]	1	4	二维HN	瞳孔、普尔钦斑	自由运动	1.23$^\circ $$\pm$0.54$^\circ $
Choi等^[70]	1	4	二维HN	瞳孔、普尔钦斑	头部固定	$1.03^\circ\sim 1.17^\circ$	单点标定
Kim等^[73]	1	4	二维HN	瞳孔、普尔钦斑	仅平转	0.76$^\circ $
Zhou等^[89]	1 (Kinect)	0	三维	虹膜、眼角	运动范围: 11.63$^\circ\times $13.44 $^\circ \times $9.43$^\circ$	1.99$^\circ $
Sun等^[28]	1 (Kinect)	0	三维	虹膜、眼角	自由运动	$1.28^\circ \sim 2.71^\circ$
Li等^[92]	1 (Kinect)	0	三维	虹膜、眼角	自由运动	水平3.0$^\circ $, 竖直4.5$^\circ $
Cristina等^[11]	1	0	三维	虹膜、眼角	运动范围(5$^\circ $内)	$1.46^\circ \sim 1.93^\circ$
Wang等^[77]	1	4	三维	瞳孔、普尔钦斑	自由运动	1.3$^\circ $	免个体标定
Lai等^[107]	2	2	三维	瞳孔、普尔钦斑	运动范围: 50mm$\times$ 25mm $\times$ 50mm	1.18$^\circ $
Bakker等^[113]	3	3	三维	瞳孔、普尔钦斑	自由运动	0.5$^\circ $
Lidegaard等^[116]	4	4	三维	瞳孔、普尔钦斑	自由运动	1.5$^\circ $	头戴式

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