Study on Methods of 3-D Gaze Tracking
Date Issued
2014
Date
2014
Author(s)
Lai, Chih-Chuan
Abstract
Gaze estimation techniques play important roles in inferring the focus of attention all the time. In this dissertation, we develop two remote 3-D gaze tracking methods. One is a
3-D model-based method that uses stereo vision with high resolution infrared imaging, which satisfies the high accuracy requirement for the use in controlled environments; the other is an appearance-based method that uses a commercial-off-the-shelf camera without requiring any dedicated hardware, which provides moderate estimation accuracy but can be used in less constrained environments.
The first part of this thesis describes a 3-D model-based method for gaze tracking. Corneal reflection (or glint) is an important feature used by many 3-D model-based methods.
However, when the operation range of a gaze tracking system is enlarged, glint feature-based (GFB) approaches will suffer from performance mainly due to the missing glint problem. Although the pupil contour feature may provide complementary information to help estimating the eye gaze, existing methods do not properly handle the cornea refraction problem which leads to inaccurate results. We describes a contour-feature based (CFB) 3-D gaze tracking method that is compatible to cornea refraction. We also
show that both the GFB approach and the CFB approach can be formulated in a unified framework and, thus, they can be easily integrated. Furthermore, it is shown that the integration of the proposed CFB method and the GFB method is necessary because the two methods provide complementary information that helps to leverage the strength of both features and provides robustness and flexibility to the system.
In the second part of this thesis, an appearance-based gaze tracking system is presented. The main difficulties for the appearance-based method are that eye appearances are varied with head motion. To overcome the difficulty, we propose a 3-D gaze tracking method combining head pose tracking and appearance-based gaze estimation. We present a robust model-based head pose tracking method guided by the eye location information and develop an automatic mechanism to deal with the problems of pose initialization, tracking loss recovery and re-initialization. We use a random forest approach to model the neighbor structure of the joint head pose and eye appearance space. l1-optimization is then used to seek for the best solution for regression from the selected neighboring samples. The constructed system is capable of analyzing the 3-D visual LoS of a person allowing the movement of his/her head and eye in a more natural manner but still provides moderate estimation accuracy.
Subjects
視線追蹤
角膜反射
亮點
瞳孔輪廓
頭部方位追蹤
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-103-D94944004-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):e509321036c8e24ca1ded262aaeb99e3