Real-Time Head Pose Estimation Based on Kalman Filter and Random Regression Forest

被引：2

作者：

Li C. ^{[1
,2
]}

Zhong F. ^{[1
,2
]}

Ma X. ^{[3
]}

Qin X. ^{[1
,2
]}

机构：

[1] School of Computer Science and Technology, Shandong University, Ji'nan

[2] Engineering Research Center of Digital Media Technology, Ministry of Education of PRC, Shandong University, Ji'nan

[3] School of Control Science and Engineering, Shandong University, Ji'nan

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2017年 / 29卷 / 12期

关键词：

Head pose estimation; Kalman filter; Random regression forest;

D O I：

10.3724/SP.J.1089.2017.16521

中图分类号：

学科分类号：

摘要：

Head pose estimation plays an essential role in many high-level face analysis tasks. However, accurate and robust pose estimation is still very challenge with existing approaches. In this paper we propose an accurate head pose estimation method based on random regression forest and Kalman filter with popular RGBD cameras such as Kinect. Firstly, the position of head in the depth image is predicted using Kalman filter, and depth patches are sampled in the prediction area. We then pass these patches through random regression forest to estimate head pose which is considered as the measurement of Kalman filter. Finally, the Kalman filter is used to combine the prediction and the measurement to obtain the final head pose. Compared with the existing random regression forest algorithm, the experimental results show that this algorithm has faster speed, better robustness and higher accuracy. © 2017, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：2309 / 2316

页数：7

共 24 条

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