期刊文献+

基于梯度和颜色直方图相融合方法的人脸跟踪 被引量:2

Gradient and color histogram fusing based head-tracking
下载PDF
导出
摘要 利用长短轴比例固定为1.4的椭圆模型模拟人脸,并融合梯度模型和颜色直方图模型进行人脸跟踪,给出了梯度模型和颜色直方图匹配度计算方法。因为两种模型之间在考虑的点集和利用的信息上具有互补性,所以提高了系统的鲁棒性。带加速度的运动预测则有效地减小了检测区域,提高了系统的速度。为降低光照变化的影响,使用一种简单的自适应亮度补偿算法,在光照较弱的条件下对待测图像进行补偿,从而提高了系统的适应性和实用性。实验表明:该方法在复杂背景中甚至在较暗的光照条件下都能取得较好的效果,可以跟踪人脸任意方向的运动,速度可以达到20帧/s以上。 This method of head-tracking uses ellipses to simulate human heads, the proportion of whose major axis and minor axis is fixed to 1.4, and it fuses intensity gradient model and color histogram model to track human heads, and the method of calculating the similarity between the template and the image to be detected using the two models is also given. Because the two models are complement to each other as to the element set concerned and the information used, this method improves the robustness of the system. Motion prediction taking acceleration into account could effectively reduce the detection area and it consequently increases the tracking speed. To eliminate the influence of lightness changing, it puts forward an algorithm of adaptive lightness compensation to compensate the image to be detected in faint lighting conditions. Experiments show that it could work well in complex background and even in bad lighting conditions, and it could track arbitrary movement of human heads, and the tracking speed could be above 20 frames per second.
出处 《吉林大学学报(信息科学版)》 CAS 2004年第5期489-493,共5页 Journal of Jilin University(Information Science Edition)
基金 国家自然科学基金资助项目(60175024) 教育部"符号计算与知识工程"重点实验室资助项目
关键词 人脸跟踪 自适应亮度补偿 梯度模型 颜色直方图 head-tracking adaptive lightness compensation gradient model color histogram
  • 相关文献

参考文献10

  • 1MARCO LA CASCIA, STAN SCLAROFF, VASSILIS ATHITSOS. Fast reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3D models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(4): 322-3
  • 2LIANG WANG, TIENIU TAN, WEIMING HU. Face tracking using motion-guided dynamic template matching [C]∥5th Asian Conference on Computer Vision. Melbourne, Australia: Asian Federation of Computer Vision Societies, 2002: 448-453.
  • 3KWOK-WAI WONG, KIN-MAN LAM, WAN-CHI SIU. A robust scheme for live detection of human faces in color images[J]. Signal Processing: Image Communication, 2003, 18:103-114.
  • 4ING-SHEEN HSIEH, KUO-CHIN FAN, CHIUHSIUN LIN. A statistic approach to the detection of human faces in color nature scene[J]. Pattern Recognition, 2002, 35 : 1583-1596.
  • 5KARIN SOBOTTKA, IOANNIS PITAS. A novel method for automatic face segmentation, facial feature extraction and tracking[J]. Signal Processing: Image Communication, 1998,12(3): 263-281.
  • 6SUMIT BASU,IRFAN ESSA, ALEX PENTLAND. Motion regularization for model based head tracking[C]∥Proceedings of the 13th IEEE International Conference on Pattern Recognition. Vienna, Austria: IEEE,1996: 611-616.
  • 7WANG CE, MICHAEL S BRANDSTEIN. A hybrid real-time face tracking system [C]∥ICASSP98. Washington, Seattle: Institute of Electrical and Electronics Engineers Signal Processing Society, 1998: 3 737-3 741.
  • 8徐一华,朱玉文,贾云得.一种人头部实时跟踪方法[J].中国图象图形学报(A辑),2002,7(1):11-15. 被引量:5
  • 9KIM J B, JUNG S W, KIM H J. Face detection by integrating multiresolution-based watersheds and skin-color model[C]∥Lecture Notes In Computer Science. London, UK: Springer-Verlag Heidelberg, 2002: 715-724.
  • 10ZHAO Hung-xin, HUANG Yea-shuan. Real-time multiple-person tracking system[C]∥16th International Conference on Pattern Recognition, Quebec, Canada: IEEE , 2002,2: 897-900.

二级参考文献9

  • 1Cordea M D, Petriu E M, Georganas N D et al, Real-time 2^1/2D head pose recovery for model-based video-coding[A], In:Proc. IEEE on Instrumemation and Measurement Technology [C]. Baltimore, MD, 2000:601-606.
  • 2Crowley J L, Berard F, Multi modal tracking of faces far video communications [A], In: Proc. IEEE on CVPR[C]. San Juan, Puerto Rico, 1997:640-645.
  • 3Jianbo Shi, Carlo Tomasi. Good leatures to track[A], In:Proc. IEEE on CVPR[C]. Seattle, WA, 1994:593-600.
  • 4Tiziano Tommasini, Andrea Fusiello, Emanuele Truceo et al. Making good features track better[A]. In: Proc. IEEE on CVPR[C]. Santa Barbara, CA,1998:178-183.
  • 5Antonio C, Brendan F, Huang T S. Detection and tracking of faces and facial features[A], In:Proc. IEEE on ICIP[C], Kobe, Japan, 1999:657-661.
  • 6Antonio C, Ricardo L, Huang T S. 3D model based head tracking[J]. In:proc. of SPIE on VCIP[C], San Jose, CA,1997. 3024: 426-434.
  • 7Marco Cascia. John Isidoro, Stan Sclaroff. Head tracking via robust registration in texture map images[A]. In :Proc. IEEE on CVPR[C]. Santa Barbara, CA.1998:508-514.
  • 8Jie Yang, Alex Waibel, Tracking human faces in real-time [R].CMU CS Technical Report, CMU-CS-95-210. 1995.
  • 9Nuria Oliver, Alex Pentland, Francois Berard. LAFTER:Lips and face real time tracker with facial expression recognition[A]. In:Proc. IEEE on CVPR[C]. San Juan, Puerto Rico, 1997:123-129.

共引文献4

同被引文献16

  • 1黄超,谢康林,杜平.基于Adaboost的快速人脸跟踪算法[J].计算机工程,2004,30(B12):373-374. 被引量:6
  • 2方帅,迟健男,徐心和.视频监控中的运动目标跟踪算法[J].控制与决策,2005,20(12):1388-1391. 被引量:16
  • 3李大湘,彭进业,邓楠.基于多模型及SVM的单人脸跟踪系统[J].微计算机应用,2006,27(2):181-184. 被引量:3
  • 4BHUIYAN Md Al-Amin, AM. PORNARAMVETH Vuthichai, MUTO Shin-yo, et al. Face detection and facial feature localization for human-machine in- terface [J]. NII Journal, 2003, 5(3).. 25-39.
  • 5ZENG W, GAO W, ZHANG T, et al. Image guarder: An intelligent detector for adult images [C]. Proceedings of the 6th Asian Conference on Computer Vision. Jeju Island, Korea, 2004: 198- 203.
  • 6ISARD M, BLAKE A. Condensation-conditional density propagation for visual tracking [ J ]. International Journal of Computer Vision, 1998, 29(1) :5-28.
  • 7SHAN Cai-feng, WEI Yu-cheng, TAN Tie-niu, et al. Real time hand tracking by combining particle filtering and mean shift [C]. Sixth IEEE Interna- tional Conference on Automatic Face and Gesture Recognition. Seoul, Korea: IEEE Computer Socie- ty, 2004: 669-674.
  • 8COMANICIU D, RAMESH V. Mean shift and op- timal prediction for efficient object tracking[C]. Proc of the IEEE International Conference on Image Processing, Vancouver, Canada, 2000: 70-73.
  • 9高文.多功能感知机的框架机构[C]//第二届中国计算机智能接口与智能应用学术会议论文集.威海,1995:7-20.
  • 10Sung Kahkay, Tomaso Poggio. Example-based learning for view-based human face detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(1):39-42.

引证文献2

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部