Abstract
In this paper, we propose a real-time framework that can not only estimate location of hands within a RGB image but also their corresponding 3D joint coordinates and their hand side determination of left or right handed simultaneously. Most of the recent methods on hand pose analysis from monocular images only focus on the 3D coordinates of hand joints, which cannot give a full story to users or applications. Moreover, to meet the demands of applications such as virtual reality or augmented reality, a first-person viewpoint hand pose dataset is needed to train our proposed CNN. Thus, we collect a synthetic RGB dataset captured in an egocentric view with the help of Unity, a 3D engine. The synthetic dataset is composed of hands with various posture, skin color and size. We provide 21 joint annotations including 3D coordinates, 2D locations, and corresponding hand side which is left hand or right hand for each hand within an image.
Original language | English |
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Title of host publication | Pattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers |
Editors | Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan |
Publisher | Springer |
Pages | 224-237 |
Number of pages | 14 |
ISBN (Print) | 9783030412982 |
DOIs | |
State | Published - 2020 |
Externally published | Yes |
Event | 5th Asian Conference on Pattern Recognition, ACPR 2019 - Auckland, New Zealand Duration: 26 11 2019 → 29 11 2019 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12047 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 5th Asian Conference on Pattern Recognition, ACPR 2019 |
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Country/Territory | New Zealand |
City | Auckland |
Period | 26/11/19 → 29/11/19 |
Bibliographical note
Publisher Copyright:© 2020, Springer Nature Switzerland AG.
Keywords
- Convolutional neural network
- Hand pose estimation
- Synthetic data