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

    抜粋

    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.

    元の言語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
    出版者Springer-Verlag
    ページ310-320
    ページ数11
    ISBN(印刷物)9783030014230
    DOI
    出版物ステータスPublished - 2018 1 1
    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(印刷物)0302-9743
    ISSN(電子版)1611-3349

    Other

    Other27th International Conference on Artificial Neural Networks, ICANN 2018
    Greece
    Rhodes
    期間18/10/418/10/7

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • これを引用

    Elbasiony, R., Gomaa, W., & Ogata, T. (2018). Deep 3D pose dictionary: 3D human pose estimation from single RGB image using deep convolutional neural network. : Y. Manolopoulos, B. Hammer, V. Kurkova, L. Iliadis, & I. Maglogiannis (版), 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); 巻数 11141 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-030-01424-7_31