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

Reda Elbasiony, Walid Gomaa, Tetsuya Ogata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
EditorsYannis Manolopoulos, Barbara Hammer, Vera Kurkova, Lazaros Iliadis, Ilias Maglogiannis
PublisherSpringer Verlag
Pages310-320
Number of pages11
ISBN (Print)9783030014230
DOIs
Publication statusPublished - 2018
Event27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece
Duration: 2018 Oct 42018 Oct 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11141 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other27th International Conference on Artificial Neural Networks, ICANN 2018
Country/TerritoryGreece
CityRhodes
Period18/10/418/10/7

Keywords

  • 3D pose estimation
  • CNN
  • Deep learning
  • Human3.6m

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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