8 March 2018 Hand pose estimation in depth image using CNN and random forest
Author Affiliations +
Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091K (2018) https://doi.org/10.1117/12.2288114
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
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.
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Xi Chen, Xi Chen, Zhiguo Cao, Zhiguo Cao, Yang Xiao, Yang Xiao, Zhiwen Fang, Zhiwen Fang, } "Hand pose estimation in depth image using CNN and random forest", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091K (8 March 2018); doi: 10.1117/12.2288114; https://doi.org/10.1117/12.2288114

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