Fast and Accurate 3D Hand Pose Estimation via Recurrent Neural Network for Capturing Hand Articulations

Cheol Hwan Yoo, Seowon Ji, Yong Goo Shin, Seung Wook Kim, Sung Jea Ko

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)

    Abstract

    3D hand pose estimation from a single depth image plays an important role in computer vision and human-computer interaction. Although recent hand pose estimation methods using convolution neural network (CNN) have shown notable improvements in accuracy, most of them have a limitation that they rely on a complex network structure without fully exploiting the articulated structure of the hand. A hand, which is an articulated object, is composed of six local parts: the palm and five independent fingers. Each finger consists of sequential-joints that provide constrained motion, referred to as a kinematic chain. In this paper, we propose a hierarchically-structured convolutional recurrent neural network (HCRNN) with six branches that estimate the 3D position of the palm and five fingers independently. The palm position is predicted via fully-connected layers. Each sequential-joint, i.e. finger position, is obtained using a recurrent neural network (RNN) to capture the spatial dependencies between adjacent joints. Then the output features of the palm and finger branches are concatenated to estimate the global hand position. HCRNN directly takes the depth map as an input without a time-consuming data conversion, such as 3D voxels and point clouds. Experimental results on public datasets demonstrate that the proposed HCRNN not only outperforms most 2D CNN-based methods using the depth image as their inputs but also achieves competitive results with state-of-the-art 3D CNN-based methods with a highly efficient running speed of 285 fps on a single GPU.

    Original languageEnglish
    Article number9114986
    Pages (from-to)114010-114019
    Number of pages10
    JournalIEEE Access
    Volume8
    DOIs
    Publication statusPublished - 2020

    Bibliographical note

    Publisher Copyright:
    © 2013 IEEE.

    Keywords

    • 3D hand pose estimation
    • hand articulations
    • recurrent neural network

    ASJC Scopus subject areas

    • General Computer Science
    • General Materials Science
    • General Engineering

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