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Communication Dans Un Congrès Année : 2019

An Extensible Deep Architecture for Action Recognition Problem

Résumé

Human action Recognition has been extensively addressed by deep learning. However, the problem is still open and many deep learning architectures show some limits, such as extracting redundant spatio-temporal informations, using hand-crafted features, and instability of proposed networks on different datasets. In this paper, we present a general method of deep learning for the human action recognition. This model fits on any type of database and we apply it on CAD-120 which is a complex dataset. Our model thus clearly improves in two aspects. The first aspect is on the redundant informations and the second one is the generality and the multi-functionality application of our deep architecture. Our model uses only raw data for human action recognition and the approach achieves state-of-the-art action classification performance. Figure 1: Pipline of human action recognition using deep learning method extracted from (TEIVAS, 2017). The upper box shows the training phase and the lower one the testing phase.
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Dates et versions

hal-02285781 , version 1 (13-09-2019)

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  • HAL Id : hal-02285781 , version 1

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Isaac Sanou, Donatello Conte, Hubert Cardot. An Extensible Deep Architecture for Action Recognition Problem. 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2019), Feb 2019, Prague, Czech Republic. ⟨hal-02285781⟩
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