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A pyramidal deep learning architecture for human action recognition

Xie, L and Pan, W and Tang, C and Hu, H (2014) 'A pyramidal deep learning architecture for human action recognition.' International Journal of Modelling, Identification and Control, 21 (2). 139 - 146. ISSN 1746-6172

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Abstract

This paper proposes a pyramidal deep learning architecture for human action recognition based on depth images from a 3D vision sensor. This method consists of three steps: 1) pre-processing depth image; 2) building a hidden deep neural network; 3) pattern recognition. A novel pyramidal stacked de-noising auto-encoder (pSDAE) is proposed to build a deep neural network so that its weights can be learnt layer by layer. A feed-forward neural network based on the deep learned weights is trained to classify each action pattern. Based on the experimental results from the Kinect dataset of human actions sampled in experiments, it is clear that the proposed approach outperforms the existing classical classify method. The robust experiment results on the Weizmann dataset show the good expansibility of the proposed method. Copyright © 2014 Inderscience Enterprises Ltd.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 23 Jul 2015 10:27
Last Modified: 23 Jan 2019 00:18
URI: http://repository.essex.ac.uk/id/eprint/14420

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