Classification of human actions using pose-based features and stacked auto encoder

Ijjina, E P and C, Krishna Mohan (2016) Classification of human actions using pose-based features and stacked auto encoder. Pattern Recognition Letters, 83 (Part 3). pp. 268-277. ISSN 0167-8655

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Abstract

In this paper, we propose a method for classification of human actions using pose based features. We demonstrate that statistical information of key movements of actions can be utilized in designing an efficient input representation, using fuzzy membership functions. The ability of stacked auto encoder to learn the underlying features of input data is exploited to recognize human actions. The efficacy of the proposed approach is demonstrated on CMU MOCAP and Berkeley MHAD datasets.

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IITH Creators:
IITH CreatorsORCiD
C, Krishna MohanUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Human action recognition; Stacked auto encoder; Pose-based features; Fuzzy membership functions
Subjects: Computer science > Big Data Analytics
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 05 Apr 2016 06:47
Last Modified: 01 Sep 2017 09:06
URI: http://raiithold.iith.ac.in/id/eprint/2266
Publisher URL: https://doi.org/10.1016/j.patrec.2016.03.021
OA policy: http://www.sherpa.ac.uk/romeo/issn/0167-8655/
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