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Enhancing human action recognition via structural average curves analysis

Enhancing human action recognition via structural average curves analysis Human action recognition typically requires a large amount of training samples, which is often expensive and time-consuming to create. In this paper, we present a novel approach for enhancing human actions with a limited number of samples via structural average curves analysis. Our approach first learns average sequences from each pair of video samples for every action class and then gather them with original video samples together to form a new training set. Action modeling and recognition are proposed to be performed with the resulting new set. Our technique was evaluated on four benchmarking datasets. Our classification results are superior to those obtained with the original training sets, which suggests that the proposed method can potentially be integrated with other approaches to further improve their recognition performances. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Signal, Image and Video Processing" Springer Journals

Enhancing human action recognition via structural average curves analysis

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References (28)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Computer Science; Image Processing and Computer Vision; Signal,Image and Speech Processing; Computer Imaging, Vision, Pattern Recognition and Graphics; Multimedia Information Systems
ISSN
1863-1703
eISSN
1863-1711
DOI
10.1007/s11760-018-1311-z
Publisher site
See Article on Publisher Site

Abstract

Human action recognition typically requires a large amount of training samples, which is often expensive and time-consuming to create. In this paper, we present a novel approach for enhancing human actions with a limited number of samples via structural average curves analysis. Our approach first learns average sequences from each pair of video samples for every action class and then gather them with original video samples together to form a new training set. Action modeling and recognition are proposed to be performed with the resulting new set. Our technique was evaluated on four benchmarking datasets. Our classification results are superior to those obtained with the original training sets, which suggests that the proposed method can potentially be integrated with other approaches to further improve their recognition performances.

Journal

"Signal, Image and Video Processing"Springer Journals

Published: May 29, 2018

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