Pose guided structured region ensemble network for cascaded hand pose estimation

NEUROCOMPUTING

卷:395

Article

Chen, Xinghao¹  Wang, Guijin¹  Guo, Hengkai^2,3  Zhang, Cairong¹ 

[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

[2] ByteDance AI Lab, Beijing, Peoples R China

[3] Tsinghua Univ, Dept EE, Beijing, Peoples R China

关键词: Hand pose estimation;    Convolutional neural network;    Human computer interaction;    Depth images;   

DOI  :  10.1016/j.neucom.2018.06.097

来源: Elsevier

【 摘 要 】

Hand pose estimation from single depth images is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural networks, accurate hand pose estimation is still a challenging problem. In this paper we propose a novel approach named as pose guided structured region ensemble network (Pose-REN) to boost the performance of hand pose estimation. Under the guidance of an initially estimated pose, the proposed method extracts regions from the feature maps of convolutional neural network and generates more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by tree-structured fully connections to regress the refined hand pose. The final hand pose is obtained by an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms. (C) 2019 Elsevier B.V. All rights reserved.

【 授权许可】

Free