Head pose estimation with combined 2D SIFT and 3D HOG features

Bingjie Wang, Wei Liang, Yucheng Wang, Yan Liang

科研成果: 书/报告/会议事项章节会议稿件同行评审

34 引用 (Scopus)

摘要

In this paper, an approach is presented to estimate the 3D position and orientation of head from RGB and depth images captured by a commercial sensor Kinect. We use 2D Scale-invariant feature transform (SIFT) features together with 3D histogram of oriented gradients (HOG) features which are extracted in a pair of RGB and depth images captured synchronously, named SIFT-HOG features, to improve the robustness and accuracy of head pose estimation. We apply random forests to formulate pose estimation as a regression problem, due to their power for handling large training data and the high mapping speed. And then the mean-shift method is employed to refine the result obtained by the random forests. The experiment results demonstrate that our approach of head pose estimation is efficient.

源语言英语
主期刊名Proceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013
650-655
页数6
DOI
出版状态已出版 - 2013
活动2013 7th International Conference on Image and Graphics, ICIG 2013 - Qingdao, Shandong, 中国
期限: 26 7月 201328 7月 2013

出版系列

姓名Proceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013

会议

会议2013 7th International Conference on Image and Graphics, ICIG 2013
国家/地区中国
Qingdao, Shandong
时期26/07/1328/07/13

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