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Saliency Model Based Head Pose Estimation by Sparse Optical Flow
Tao Xu; Yunhong Wang; Zhaoxiang Zhang; Chao Wang
2011-11-28
会议名称1st Asian Conference on Pattern Recognition
会议录名称ACPR 2011
会议日期28th November 2011
会议地点Beijing, China
摘要Head pose plays an important role in Human-Computer interaction, and its estimation is a challenge problem compared to face detection and recognition in computer vision. In this paper, a novel and efficient method is proposed to estimate head pose in real-time video sequences. A saliency model based segmentation method is used not only to extract feature points of face, but also to update and rectify the location of feature points when missing happened. This step also gives a benchmark for vector generation in pose estimation. In subsequent frames feature points will be tracked by sparse optical flow method and head pose can be determined from vectors generated by feature points between successive frames. Via a voting scheme, these vectors with angle and length can give a robust estimation of the head pose. Compared with other methods, annotated training data set and training procedure is not essential in our method. Initialization and re-initialization can be done automatically and are robust for profile head pose. Experimental results show an efficient and robust estimation of the head pose.
关键词Face Estimation Magnetic Heads Optical Imaging Computer Vision Skin
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13276
专题类脑智能研究中心
通讯作者Zhaoxiang Zhang
推荐引用方式
GB/T 7714
Tao Xu,Yunhong Wang,Zhaoxiang Zhang,et al. Saliency Model Based Head Pose Estimation by Sparse Optical Flow[C],2011.
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