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一种快速的非侵入式眼动跟踪方法

Rapid and Non-Intrusive Eye-Tracking Method
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摘要 针对眼控人机交互系统对实时性要求较高的特点,提出了一种快速、非侵入式的眼动跟踪方法。首先融合CamShift跟踪算法和AdaBoost型目标检测方法快速地分割出眼睛所在矩形区域;然后在第一帧视频图像中精确定位虹膜并建立虹膜模板;最后通过放大或缩小虹膜模板自适应地在后续各帧视频图像中搜索匹配以预测虹膜位置。虹膜中心相对眼角或鼻孔的位移可用于控制鼠标指针在屏幕上移动。在分辨率为640×480的视频上,取得了32帧/秒的跟踪速度,正确率达到95.5%。实验结果表明方法是有效的,可直接应用于眼控人机交互系统。 To meet the real-time requirement of eye-controlled human-computer interaction systems,a fast and non-intrusive eye-tracking method was proposed.Firstly,CamShift algorithm and AdaBoost algorithm were combined to extract eye regions.Secondly,iris was located from the eye region in the first video frame.And the iris region was used as a template.Finally,the iris template was scaled according to new size of eye region in the subsequent video frames.Then the scaled template was used for matching in the eye region.The relative displacement between iris center and canthus or nostril can be used to control the movement of mouse pointer on computer screen.The experiments on a video of resolution at 640*480 result a tracking speed of 32 frames per second,and the correct rate is 95.5%.Results prove the effectiveness and efficiency of this method.So,this method can be directly applied to eye-controlled human-computer interaction systems.
出处 《计算机系统应用》 2012年第1期172-175,共4页 Computer Systems & Applications
基金 国家自然科学基金(60873179 11005081) 浙江省自然科学基金(Y1110322) 浙江省教育厅科技项目(Y201016244) 浙江省青年教师资助计划(2010) 校科研启动项目(QTJ09004 QTJ09009)
关键词 无障碍技术 非侵入式眼动跟踪 虹膜定位 CAMSHIFT算法 ADABOOST算法 accessible technology Non-Intrusive eye tracking iris location CamShift algorithm AdaBoost algorithm
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  • 1吴暾华,周昌乐.快速人脸检测系统的设计与实现[J].计算机应用,2005,25(10):2351-2353. 被引量:9
  • 2Chart T F, Vese L A. Active contours without edges[J]. IEEE Transactions on Image Processing,2001,10(2):266 - 277.
  • 3Song Gao, Tien D Bui. Image segmentation and selective smoothing by using mumford-shah model[J]. IEEE Transactions on Image Processing, 2005,14(10): 1537 - 1549.
  • 4C Tomasi, T Kanade. Detection and Tracking of Point Features [R]. Technical Report CMU-CS-91-132, Pittsburgh, USA: Carnegie Mellon University, 1991.
  • 5Sethian J A. Curvature and evolution of fronts[J]. Commun Math Phys, 1985,101(4) :487 - 499.
  • 6Osher S, Sethian J A. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations[J]. Journal of Computer Physics, 1988,79(1): 12 - 49.
  • 7Pietro Perona, Jitendra Malik. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Trans on Pattern Analysis and Matching Intelligence, 1990,12(7) :629 - 639.
  • 8Viola P, Jones M. Robust real-time object detection[A]. Proc of the IEEE, ICCV Workshop on Statistical and Computational Theories of Vision[C]. USA(Vancouver) : IEEE 2001.
  • 9Ming-Hsuan Yang. Recent advances in face detection[A] .Proc of tile IEEE ICPR 2004 Tutorial [C]. United Kingdom ( Cambridge ) : IEEE, 2004.
  • 10H A Rowley. Neural network-based human face detection[D]. Pittsburgh, USA:Carnegie Mellon University, 1999.

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