@inproceedings{3375f0f12f054bd78ddfbd5c86fc21e2,
title = "Deep 3D pose dictionary: 3D human pose estimation from single RGB image using deep convolutional neural network",
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.",
keywords = "3D pose estimation, CNN, Deep learning, Human3.6m",
author = "Reda Elbasiony and Walid Gomaa and Tetsuya Ogata",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-030-01424-7_31",
language = "English",
isbn = "9783030014230",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "310--320",
editor = "Yannis Manolopoulos and Barbara Hammer and Vera Kurkova and Lazaros Iliadis and Ilias Maglogiannis",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings",
note = "27th International Conference on Artificial Neural Networks, ICANN 2018 ; Conference date: 04-10-2018 Through 07-10-2018",
}