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Proceedings Paper

Hand pose estimation in depth image using CNN and random forest
Author(s): Xi Chen; Zhiguo Cao; Yang Xiao; Zhiwen Fang
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Paper Abstract

Thanks to the availability of low cost depth cameras, like Microsoft Kinect, 3D hand pose estimation attracted special research attention in these years. Due to the large variations in hand`s viewpoint and the high dimension of hand motion, 3D hand pose estimation is still challenging. In this paper we propose a two-stage framework which joint with CNN and Random Forest to boost the performance of hand pose estimation. First, we use a standard Convolutional Neural Network (CNN) to regress the hand joints` locations. Second, using a Random Forest to refine the joints from the first stage. In the second stage, we propose a pyramid feature which merges the information flow of the CNN. Specifically, we get the rough joints` location from first stage, then rotate the convolutional feature maps (and image). After this, for each joint, we map its location to each feature map (and image) firstly, then crop features at each feature map (and image) around its location, put extracted features to Random Forest to refine at last. Experimentally, we evaluate our proposed method on ICVL dataset and get the mean error about 11mm, our method is also real-time on a desktop.

Paper Details

Date Published: 8 March 2018
PDF: 8 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091K (8 March 2018);
Show Author Affiliations
Xi Chen, Huazhong Univ. of Science and Technology (China)
Zhiguo Cao, Huazhong Univ. of Science and Technology (China)
Yang Xiao, Huazhong Univ. of Science and Technology (China)
Zhiwen Fang, Hunan Univ. of Humanities (China)

Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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