Power difference template for action recognition

Power difference template for action recognition This paper proposes power difference template as a new spatial-temporal representation for action recognition. Specifically, spatial power features are first extracted according to the transform of Gaussian convolution on gradients between logarithmic and exponential domain. Using the forward–backward frame power difference method, we thus present normalized projection histogram (NPH) to characterize segmented action spatial features by normalizing histogram of the 2D horizontal–vertical projections. Furthermore, from the perspective of energy conservation, motion kinetic velocity (MKV) is introduced as a supplement for representing temporal relationships of power features by supposing that the variation of power is produced by motion in the form of kinetic energy. Our power difference template fusing NPH and MKV is further integrated to a bag of word model for training and testing under a support vector machine framework. Experiments on KTH, UCF Sports, UCF101 and HMDB datasets demonstrate the effectiveness of the proposed algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Vision and Applications Springer Journals

Power difference template for action recognition

Loading next page...
 
/lp/springer_journal/power-difference-template-for-action-recognition-wn0GBfHPdU
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Computer Science; Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks
ISSN
0932-8092
eISSN
1432-1769
D.O.I.
10.1007/s00138-017-0848-0
Publisher site
See Article on Publisher Site

Abstract

This paper proposes power difference template as a new spatial-temporal representation for action recognition. Specifically, spatial power features are first extracted according to the transform of Gaussian convolution on gradients between logarithmic and exponential domain. Using the forward–backward frame power difference method, we thus present normalized projection histogram (NPH) to characterize segmented action spatial features by normalizing histogram of the 2D horizontal–vertical projections. Furthermore, from the perspective of energy conservation, motion kinetic velocity (MKV) is introduced as a supplement for representing temporal relationships of power features by supposing that the variation of power is produced by motion in the form of kinetic energy. Our power difference template fusing NPH and MKV is further integrated to a bag of word model for training and testing under a support vector machine framework. Experiments on KTH, UCF Sports, UCF101 and HMDB datasets demonstrate the effectiveness of the proposed algorithm.

Journal

Machine Vision and ApplicationsSpringer Journals

Published: Jun 14, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off