移动设备交互环境下的注视点感知计算方法
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摘要
为了提高移动设备上眼动跟踪的精度和效率、降低硬件成本,提出基于注视点回忆的眼动数据感知计算方法.首先利用人的短时记忆特性建立注视点回忆和自我报告机制,要求用户点击移动设备屏幕来提交注视点位置数据;然后基于支持向量回归方法建立注视点数据误差补偿模型,对用户回忆和提交的注视点数据进行校正,进一步提高数据精度.为了验证数据误差补偿模型的效果,设计并开展了用户实验,结果表明,使用数据误差补偿模型后,对于不同类型的测试任务和测试图片,注视点回忆数据精度提高15%～40%.
To improve the accuracy and efficiency of eye tracking on mobile device, and to reduce the cost of hardware, an eye movement data perception and computing method based on gaze recall was proposed. Firstly, setup the mechanism of gaze recall and self reporting based on short-term memory of human being, and then asked users to tap the screen of mobile device, and submit the gaze position data. Proposed the model of gaze data error compensation based on support vector regression, and used this model to correct the gaze data users recalled and submitted, so as to improve the data accuracy. To validate the gaze data error compensation model, user study was designed and conducted, and the results showed that after using the gaze data error compensation model, for different test tasks and images, accuracy of the gaze recalled data was improved by 15 % to 40 %.
To improve the accuracy and efficiency of eye tracking on mobile device, and to reduce the cost of hardware, an eye movement data perception and computing method based on gaze recall was proposed. Firstly, setup the mechanism of gaze recall and self reporting based on short-term memory of human being, and then asked users to tap the screen of mobile device, and submit the gaze position data. Proposed the model of gaze data error compensation based on support vector regression, and used this model to correct the gaze data users recalled and submitted, so as to improve the data accuracy. To validate the gaze data error compensation model, user study was designed and conducted, and the results showed that after using the gaze data error compensation model, for different test tasks and images, accuracy of the gaze recalled data was improved by 15 % to 40 %.
引文
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[11]Sugano Y,Zhang X C,Bulling A.AggreGaze:collective estimation of audience attention on public displays[C]//Proceedings of the 29th Annual Symposium on User Interface Software and Technology.New York:ACM Press,2016:821-831
[12]Xu P M,Sugano Y,Bulling A.Spatio-temporal modeling and prediction of visual attention in graphical user interfaces[C]//Proceedings of the CHI Conference on Human Factors in Computing Systems.New York:ACM Press,2016:3299-3310
[13]Krafka K,Khosla A,Kellnhofer P,et al.Eye tracking for everyone[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2016:2176-2184
[14]Aznar F,Pujol M,Rizo R.Generating saliency maps using human based computation agents[C]//Proceedings of the Current Topics in Artificial Intellifence,and the 13th Conference of the Spanish Association for Artificial Intelligence.Heidelberg:Springer-Verlag Press,2009:252-260
[15]Rudoy D,Goldman D B,Shechtman E,et al.Crowdsourcing gaze data collection[J].Encyclopedic Dictionary of Polymers,2012,194(2):367-377
[16]Smola A J,Sch?lkopf B.A tutorial on support vector regression[J].Statistics and Computing,2004,14(3):199-222
[17]Wang Dingcheng,Fang Tingjian,Tang Yi,et al.Review of support vector machines regression theory and control[J].Pattern Recognition and Artificial Intelligence,2003,16(2):192-197(in Chinese)(王定成,方廷健,唐毅,等.支持向量机回归理论与控制的综述[J].模式识别与人工智能,2003,16(2):192-197)
[18]Drucker H,Burges C J C,Kaufman L,et al.Support vector regression machines[C]//Proceedings of Advances in Neural Information Processing Systems.Cambridge:MIT Press,1997:155-161
[19]Gunn S R.Support vector machines for classification and regression[R].Southampton:University of Southampton,1998
[20]Shawe-Taylor J,Cristianini N.Kernel methods for pattern analysis[M].New York:Cambridge University Press,2004
[21]Xie Hong,Zhao Hongye.An improved KNN algorithm based on Chi-square distance measure[J].Applied Science and Technology,2015,42(1):10-14(in Chinese)(谢红,赵洪野.基于卡方距离度量的改进KNN算法[J].应用科技,2015,42(1):10-14)
[22]Zhu Yapei,Sun Min.On the research of the option of the optimal kernel of density estimator[J].Journal of Tianshui Normal University,2015,35(2):12-14(in Chinese)(朱亚培,孙敏.基于核密度估计优核选择的研究[J].天水师范学院学报,2015,35(2):12-14)
[23]Zhang Yumin.Comparative study of probability density function estimation based on different kernel function[D].Baoding:Hebei University,2010(in Chinese)(张玉敏.基于不同核函数的概率密度函数估计比较研究[D].保定:河北大学,2010)
[24]Cheng Shiwei,Sun Zhiqiang.An approach to eye tracking for mobile device based interaction[J].Journal of Computer-Aided Design&Computer Graphics,2014,26(8):1354-1361(in Chinese)(程时伟,孙志强.用于移动设备人机交互的眼动跟踪方法[J].计算机辅助设计与图形学学报,2014,26(8):1354-1361)
(1)http://www.image-net.org
(2)https://www.flickr.com/
[2]Cheng S W,Sun Z Q,Ma X J,et al.Social eye tracking:gaze recall with online crowds[C]//Proceedings of the 18th ACMConference on Computer Supported Cooperative Work&Social Computing.New York:ACM Press,2015:454-463
[3]Vapnik V,Golowich S,Smola A J.Support vector method for function approximation,regression estimation,and signal processing[C]//Proceedings of Advances in Neural Information Processing Systems.Cambridge:MIT Press,1997:281-287
[4]Liu Yang,Yang Wei,Zheng Fengbin.Cognitive neural mechanisms and saliency computational model of visual selective attention[J].Journal of Chinese Computer Systems,2014,35(3):584-589(in Chinese)(刘扬,杨伟,郑逢斌.视觉选择性注意的认知神经机制与显著性计算模型[J].小型微型计算机系统,2014,35(3):584-589)
[5]Blackwell A F,Jansen A R,Marriott K.Restricted focus viewer:a tool for tracking visual attention[C]//Proceedings of the 1st International Conference on Theory and Application of Diagrams.London:Springer,2000:162-177
[6]Chen M C,Anderson J R,Sohn M H.What can a mouse cursor tell us more?Correlation of eye/mouse movements on web browsing[C]//Proceedings of the 19th Annual ACM Conference on Human Factors in Computing Systems.New York:ACM Press,2001:281-282
[7]Borji A,Itti L.State-of-the-art in visual attention modeling[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):185-207
[8]Zhang L,Tong M H,Marks T K,et al.SUN:a Bayesian framework for saliency using natural statistics[J].Journal of Vision,2008,8(7):32.1-20
[9]Lebreton P,M?ki T,Skodras E,et al.Bridging the gap between eye tracking and crowdsourcing[C]//Proceedings of SPIE.Bellingham:Society of Photo-Optical Instrumentation Engineers Press,2015,9394:Article No.14
[10]Zhang X C,Sugano Y,Fritz M,et al.Appearance-based gaze estimation in the wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2015:4511-4520
[11]Sugano Y,Zhang X C,Bulling A.AggreGaze:collective estimation of audience attention on public displays[C]//Proceedings of the 29th Annual Symposium on User Interface Software and Technology.New York:ACM Press,2016:821-831
[12]Xu P M,Sugano Y,Bulling A.Spatio-temporal modeling and prediction of visual attention in graphical user interfaces[C]//Proceedings of the CHI Conference on Human Factors in Computing Systems.New York:ACM Press,2016:3299-3310
[13]Krafka K,Khosla A,Kellnhofer P,et al.Eye tracking for everyone[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2016:2176-2184
[14]Aznar F,Pujol M,Rizo R.Generating saliency maps using human based computation agents[C]//Proceedings of the Current Topics in Artificial Intellifence,and the 13th Conference of the Spanish Association for Artificial Intelligence.Heidelberg:Springer-Verlag Press,2009:252-260
[15]Rudoy D,Goldman D B,Shechtman E,et al.Crowdsourcing gaze data collection[J].Encyclopedic Dictionary of Polymers,2012,194(2):367-377
[16]Smola A J,Sch?lkopf B.A tutorial on support vector regression[J].Statistics and Computing,2004,14(3):199-222
[17]Wang Dingcheng,Fang Tingjian,Tang Yi,et al.Review of support vector machines regression theory and control[J].Pattern Recognition and Artificial Intelligence,2003,16(2):192-197(in Chinese)(王定成,方廷健,唐毅,等.支持向量机回归理论与控制的综述[J].模式识别与人工智能,2003,16(2):192-197)
[18]Drucker H,Burges C J C,Kaufman L,et al.Support vector regression machines[C]//Proceedings of Advances in Neural Information Processing Systems.Cambridge:MIT Press,1997:155-161
[19]Gunn S R.Support vector machines for classification and regression[R].Southampton:University of Southampton,1998
[20]Shawe-Taylor J,Cristianini N.Kernel methods for pattern analysis[M].New York:Cambridge University Press,2004
[21]Xie Hong,Zhao Hongye.An improved KNN algorithm based on Chi-square distance measure[J].Applied Science and Technology,2015,42(1):10-14(in Chinese)(谢红,赵洪野.基于卡方距离度量的改进KNN算法[J].应用科技,2015,42(1):10-14)
[22]Zhu Yapei,Sun Min.On the research of the option of the optimal kernel of density estimator[J].Journal of Tianshui Normal University,2015,35(2):12-14(in Chinese)(朱亚培,孙敏.基于核密度估计优核选择的研究[J].天水师范学院学报,2015,35(2):12-14)
[23]Zhang Yumin.Comparative study of probability density function estimation based on different kernel function[D].Baoding:Hebei University,2010(in Chinese)(张玉敏.基于不同核函数的概率密度函数估计比较研究[D].保定:河北大学,2010)
[24]Cheng Shiwei,Sun Zhiqiang.An approach to eye tracking for mobile device based interaction[J].Journal of Computer-Aided Design&Computer Graphics,2014,26(8):1354-1361(in Chinese)(程时伟,孙志强.用于移动设备人机交互的眼动跟踪方法[J].计算机辅助设计与图形学学报,2014,26(8):1354-1361)
(1)http://www.image-net.org
(2)https://www.flickr.com/