Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139907
Title: Hough forest with optimized leaves for global hand pose estimation with arbitrary postures
Authors: Liang, Hui
Yuan, Junsong
Lee, Jun
Ge, Liuhao
Thalmann, Daniel
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Liang, H., Yuan, J., Lee, J., Ge, L., & Thalmann, D. (2019). Hough forest with optimized leaves for global hand pose estimation with arbitrary postures. IEEE Transactions on Cybernetics, 49(2), 527-541. doi:10.1109/TCYB.2017.2779800
Journal: IEEE Transactions on Cybernetics
Abstract: Vision-based hand pose estimation is important in human-computer interaction. While many recent works focus on full degree-of-freedom hand pose estimation, robust estimation of global hand pose remains a challenging problem. This paper presents a novel algorithm to optimize the leaf weights in a Hough forest to assist global hand pose estimation with a single depth camera. Different from traditional Hough forest, we propose to learn the vote weights stored at the leaf nodes of a forest in a principled way to minimize average pose prediction error, so that ambiguous votes are largely suppressed during prediction fusion. Experiments show that the proposed method largely improves pose estimation accuracy with optimized leaf weights on both synthesis and real datasets and performs favorably compared to state-of-the-art convolutional neural network-based methods. On real-world depth videos, the proposed method demonstrates improved robustness compared to several other recent hand tracking systems from both industry and academy. Moreover, we utilize the proposed method to build virtual/augmented reality applications to allow users to manipulate and examine virtual objects with bare hands.
URI: https://hdl.handle.net/10356/139907
ISSN: 2168-2267
DOI: 10.1109/TCYB.2017.2779800
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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