Human action recognition in RGB-D videos using motion sequence information and deep learning

Ijjina, E P and C, Krishna Mohan (2017) Human action recognition in RGB-D videos using motion sequence information and deep learning. Pattern Recognition. ISSN 0031-3203

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Abstract

In this paper, we propose an approach for recognizing human actions based on motion sequence information in RGB-D video using deep learning. A new representation that gives emphasis to the key poses associated with each action is presented. The features obtained from motion in RGB and depth video streams are given as input to the convolutional neural network to learn the discriminative features. The efficacy of the proposed approach is demonstrated on MIVIA action, NATOPS gesture, SBU Kinect interaction, and Weizmann datasets.

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IITH Creators:
IITH CreatorsORCiD
C, Krishna MohanUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Multi-modal action recognition Deep learning Motion information Extreme learning machines
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 17 Jul 2017 05:05
Last Modified: 01 Sep 2017 09:02
URI: http://raiith.iith.ac.in/id/eprint/3385
Publisher URL: https://doi.org/10.1016/j.patcog.2017.07.013
OA policy: http://www.sherpa.ac.uk/romeo/issn/0031-3203/
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