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 Jan 1
    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
    CountryGreece
    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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  • Cite this

    Elbasiony, R., Gomaa, W., & Ogata, T. (2018). Deep 3D pose dictionary: 3D human pose estimation from single RGB image using deep convolutional neural network. In Y. Manolopoulos, B. Hammer, V. Kurkova, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings (pp. 310-320). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11141 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-030-01424-7_31