视频图像序列内的视线跟踪研究
发布时间：2018-07-15 19:06
【摘要】：眼睛是感知世界的重要器官,视线方向可以反映人们感兴趣的点。视线跟踪技术可以检测出人类眼睛的注视方向,得出兴趣点。随着电子技术的不断发展,关于视线研究技术的研究越来越多,一些视线跟踪系统已经应用于人机交互领域。随着研究的不断深入,视线跟踪技术在网络的可用性、广告、包装设计和汽车工程等领域也会有很大的发展空间。但是现有的视线跟踪技术有限,视线跟踪系统存在跟踪精度较低、限制头部运动、干扰性大的缺点。针对上述问题,论文对序列内的视线跟踪技术进行了研究,减小了对使用者头部的限制,提高了系统的准确度和稳定性。 论文的主要工作成果如下： (1)把头部姿态估计方法与二维视线跟踪方法结合,利用头部姿态参数校正用于视线估计的面部特征点间的位置距离,减小因头部发生转动给视线跟踪算法带来的误差。该方法既不需要辅助设备固定头部,又提高了视线跟踪的准确度。 (2)对用于视线跟踪的头部姿态估计算法进行了研究,提出了一种三维头部姿态估计方法。在该算法中,把头部看作圆柱体,头部的转动可以看作是圆柱体的旋转。通过不断变换姿态参数,使得当前的面部纹理与参照纹理相符合,此时的参数即为当前图像的头部姿态参数。利用视频图像帧间头部图像变化较小的特点,利用前一帧图像的头部纹理估计下一帧图像头部的姿态,减少了计算量,提高了计算精度。 (3)在边缘定位方法中对亚像素技术进行了深入的研究。在视线跟踪过程中,利用亚像素技术定位面部特征点(虹膜中心和外眼角)的亚像素位置；根据检测得到的虹膜亚像素级边缘点,利用椭圆拟合方法精确定位虹膜亚像素中心点。该方法减小因二维面部图像特征点定位不够精确给视线方向估计带来的误差,提高了视线跟踪的准确度。 (4)采用简单的头部几何模型,提前采集眼睛位于屏幕固定位置时的面部特征点,根据这些特征点组成的向量与视线方向之间的对应关系,估计当前头部图像中校正后的特征点向量的视线方向。该方法计算简单且能够准确估计视线方向,使序列内视线跟踪系统能够满足实时性要求。实现了视频序列内视线跟踪系统,验证了系统的准确度和稳定性。
[Abstract]:The eye is an important organ for perceiving the world, and the direction of sight can reflect the point of interest. Eye tracking technology can detect the gaze of the human eye and get the point of interest. With the development of electronic technology, more and more researches have been made on the line of sight technology, and some line of sight tracking systems have been applied in the field of human-computer interaction. With the development of the research, the technology of line of sight tracking will have great development space in the field of network usability, advertising, packaging design and automobile engineering. However, the existing line of sight tracking technology is limited, and the tracking system has the disadvantages of low tracking accuracy, limited head movement and large interference. Aiming at the above problems, this paper studies the line-of-sight tracking technology in the sequence, reduces the limitation on the user's head, and improves the accuracy and stability of the system. The main achievements of this paper are as follows: (1) combining the head attitude estimation method with the two-dimensional line of sight tracking method, the position distance between the facial feature points used for the line of sight estimation is corrected by using the head attitude parameters. The error caused by the rotation of the head to the line of sight tracking algorithm is reduced. This method not only needs no auxiliary equipment to fix the head, but also improves the accuracy of line of sight tracking. (2) the head attitude estimation algorithm for line of sight tracking is studied, and a three-dimensional head attitude estimation method is proposed. In this algorithm, the head is regarded as a cylinder, and the rotation of the head can be regarded as the rotation of the cylinder. By constantly changing the attitude parameters, the current facial texture is consistent with the reference texture, which is the head pose parameter of the current image. Based on the small change of the head image between the video images, the head texture of the previous frame image is used to estimate the pose of the head image of the next frame, which reduces the computation cost. The calculation accuracy is improved. (3) the sub-pixel technique is studied in the edge location method. In the course of line of sight tracking, the sub-pixel position of facial feature points (iris center and outer eye corner) is located by sub-pixel technique. The ellipse fitting method is used to locate the center of the iris subpixel accurately. This method reduces the error caused by the location of feature points in two-dimensional facial images and improves the accuracy of line of sight tracking. (4) A simple head geometry model is used. According to the relationship between the vector of these feature points and the direction of line of sight, the direction of line of sight of the corrected feature point vector in the current head image is estimated according to the relationship between the vector and the direction of line of sight. The method is simple in calculation and can accurately estimate the direction of line of sight, so that the system can meet the real-time requirements. The video sequence line of sight tracking system is implemented, and the accuracy and stability of the system are verified.
【学位授予单位】：武汉理工大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TP391.41
本文编号：2125093
[Abstract]:The eye is an important organ for perceiving the world, and the direction of sight can reflect the point of interest. Eye tracking technology can detect the gaze of the human eye and get the point of interest. With the development of electronic technology, more and more researches have been made on the line of sight technology, and some line of sight tracking systems have been applied in the field of human-computer interaction. With the development of the research, the technology of line of sight tracking will have great development space in the field of network usability, advertising, packaging design and automobile engineering. However, the existing line of sight tracking technology is limited, and the tracking system has the disadvantages of low tracking accuracy, limited head movement and large interference. Aiming at the above problems, this paper studies the line-of-sight tracking technology in the sequence, reduces the limitation on the user's head, and improves the accuracy and stability of the system. The main achievements of this paper are as follows: (1) combining the head attitude estimation method with the two-dimensional line of sight tracking method, the position distance between the facial feature points used for the line of sight estimation is corrected by using the head attitude parameters. The error caused by the rotation of the head to the line of sight tracking algorithm is reduced. This method not only needs no auxiliary equipment to fix the head, but also improves the accuracy of line of sight tracking. (2) the head attitude estimation algorithm for line of sight tracking is studied, and a three-dimensional head attitude estimation method is proposed. In this algorithm, the head is regarded as a cylinder, and the rotation of the head can be regarded as the rotation of the cylinder. By constantly changing the attitude parameters, the current facial texture is consistent with the reference texture, which is the head pose parameter of the current image. Based on the small change of the head image between the video images, the head texture of the previous frame image is used to estimate the pose of the head image of the next frame, which reduces the computation cost. The calculation accuracy is improved. (3) the sub-pixel technique is studied in the edge location method. In the course of line of sight tracking, the sub-pixel position of facial feature points (iris center and outer eye corner) is located by sub-pixel technique. The ellipse fitting method is used to locate the center of the iris subpixel accurately. This method reduces the error caused by the location of feature points in two-dimensional facial images and improves the accuracy of line of sight tracking. (4) A simple head geometry model is used. According to the relationship between the vector of these feature points and the direction of line of sight, the direction of line of sight of the corrected feature point vector in the current head image is estimated according to the relationship between the vector and the direction of line of sight. The method is simple in calculation and can accurately estimate the direction of line of sight, so that the system can meet the real-time requirements. The video sequence line of sight tracking system is implemented, and the accuracy and stability of the system are verified.
【学位授予单位】：武汉理工大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TP391.41
【参考文献】
相关期刊论文 前4条
1 于琼;樊银涛;;多角度视线上的人眼定位算法[J];电脑知识与技术;2011年04期
2 张文聪;李斌;邓宏平;谭文明;庄镇泉;;视线跟踪过程中变形瞳孔的定位[J];电子与信息学报;2010年02期
3 钟志光;易建强;赵冬斌;洪义平;李新征;;一种鲁棒的只需两帧图像的姿态估计方法[J];模式识别与人工智能;2005年04期
4 徐蓉;姜峰;姚鸿勋;;流形学习概述[J];智能系统学报;2006年01期
相关博士学位论文 前1条
1 山世光;人脸识别中若干关键问题的研究[D];中国科学院研究生院（计算技术研究所）;2004年
相关硕士学位论文 前2条
1 李华明;基于中国人面貌形态学特征的人脸姿态估计方法研究[D];西北大学;2005年
2 孙昊翔;关于人脸姿态估计问题的研究[D];吉林大学;2007年
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