Deep 3D pose dictionary: 3D human pose estimation from single RGB image using deep convolutional neural network

Reda Elbasiony, Walid Gomaa, Tetsuya Ogata

研究成果: Conference contribution

抄録

In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB image based on a deep convolutional neural network (CNN). The proposed method depends on reducing the huge search space of the continuous-valued 3D human poses by discretizing and approximating these continuous poses into many discrete key-poses. These key-poses constitute more restricted search space and then can be considered as multiple-class candidates of 3D human poses. Thus, a suitable classification technique is trained using a set of 3D key-poses and their corresponding RGB images to build a model to predict the 3D pose class of an input monocular RGB image. We use deep CNN as a suitable classifier because it is proven to be the most accurate technique for RGB image classification. Our approach is proven to achieve good accuracy which is comparable to the state-of-the-art methods.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
編集者Yannis Manolopoulos, Barbara Hammer, Vera Kurkova, Lazaros Iliadis, Ilias Maglogiannis
出版社Springer Verlag
ページ310-320
ページ数11
ISBN(印刷版)9783030014230
DOI
出版ステータスPublished - 2018
イベント27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece
継続期間: 2018 10 42018 10 7

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11141 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other27th International Conference on Artificial Neural Networks, ICANN 2018
国/地域Greece
CityRhodes
Period18/10/418/10/7

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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