Large-scale Multiview 3D Hand Pose Dataset

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Title: Large-scale Multiview 3D Hand Pose Dataset
Authors: Gomez-Donoso, Francisco | Orts-Escolano, Sergio | Cazorla, Miguel
Research Group/s: Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: 3d hand pose | Multiview | Dataset | Deep learning
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 7-Dec-2018
Publisher: Elsevier
Citation: Image and Vision Computing. 2019, 81: 25-33. doi:10.1016/j.imavis.2018.12.001
Abstract: Accurate visual hand pose estimation at joint level has several applications for human-robot interaction, natural user interfaces and virtual/augmented reality applications. However, it is still an open problem being addressed by the computer vision community. Recent novel deep learning techniques may help circumvent the limitations of standard approaches. However, they require large amounts of accurate annotated data. Hand pose datasets that have been released so far present issues such as limited number of samples, inaccurate data or high-level annotations. Moreover, most of them are focused on depth-based approaches, providing only depth information (missing RGB data). In this work, we present a novel multiview hand pose dataset in which we provide hand color images and different kind of annotations for each sample, i.e. the bounding box and the 2D and 3D location on the joints in the hand. Furthermore, we introduce a simple yet accurate deep learning architecture for real-time robust 2D hand pose estimation. Then, we conduct experiments that show how the use of the proposed dataset in the training stage produces accurate results for 2D hand pose estimation using a single color camera.
Sponsor: This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by the Generalitat Valenciana project GV/2018/22 and by a Spanish grant for PhD studies ACIF/2017/243.
ISSN: 0262-8856 (Print) | 1872-8138 (Online)
DOI: 10.1016/j.imavis.2018.12.001
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Elsevier B.V.
Peer Review: si
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Appears in Collections:INV - RoViT - Artículos de Revistas

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