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Skeletal movement to color map: A novel representation for 3D action recognition with inception residual networks

Pham, Huy-Hieu and Khoudour, Louahdi and Crouzil, Alain and Zegers, Pablo and Velastin, Sergio A. Skeletal movement to color map: A novel representation for 3D action recognition with inception residual networks. (2018) In: 25th IEEE International Conference on Image Processing (ICIP 2018), 7 October 2018 - 10 October 2018 (Athens, Greece).

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Official URL: https://doi.org/10.1109/ICIP.2018.8451404

Abstract

We propose a novel skeleton-based representation for 3D action recognition in videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have been addressed: First, how to construct a robust representation that easily captures the spatial-temporal evolutions of motions from skeleton sequences. Second, how to design D-CNNs capable of learning discriminative features from the new representation in a effective manner. To address these tasks, a skeleton-based representation, namely, SPMF (Skeleton Pose-Motion Feature) is proposed. The SPMFs are built from two of the most important properties of a human action: postures and their motions. Therefore, they are able to effectively represent complex actions. For learning and recognition tasks, we design and optimize new D-CNNs based on the idea of Inception Residual networks to predict actions from SPMFs. Our method is evaluated on two challenging datasets including MSR Action3D and NTU-RGB+D. Experimental results indicated that the proposed method surpasses state-of-the-art methods whilst requiring less computation.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org This papers appears in Proceedings of ICIP 2018. Electronic ISBN: 978-1-4799-7061-2 Electronic ISSN: 2381-8549 The original PDF of the article can be found at: https://ieeexplore.ieee.org/abstract/document/8451404 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Aparnix (CHILE)
Other partners > Centre d'études et d'expertise sur les risques, l'environnement, la mobilité et l'aménagement - CEREMA (FRANCE)
Other partners > Queen Mary University of London - QMUL (UNITED KINGDOM)
Other partners > Universidad Carlos III de Madrid - UC3M (SPAIN)
Laboratory name:
Funders:
Cerema Research Center - Institut de Recherche en Informatique de Toulouse - IRIT, Université de Toulouse - UPS (France)
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Deposited On:24 Sep 2019 09:24

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