Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences

Learning Cross-domain Dictionary Pairs for Human Action Recognition

Authors
Bingbing Zhang, Dongcheng Shi, Kang Ni, Chao Liang
Corresponding Author
Bingbing Zhang
Available Online October 2015.
DOI
10.2991/iwmecs-15.2015.84How to use a DOI?
Keywords
Human action recognition, Local motion pattern, Dictionary learning.
Abstract

This paper present a cross domain dictionary learning way, via the introduction of auxiliary domain, as the extra knowledge, the intra class diversity of the original training set (also known as the target domain) is effectively enhanced. Firstly, use local motion pattern feature as a low-level feature descriptor, and then through a cross domain reconstructive dictionary pair learning, which brings the original target data and the auxiliary domain data into the same feature space to get corresponding sparse codes of each human action categories. Finally, classification and recognition is carried on the human action representation. Using the UCF YouTube data set as the original training set and the HMDB51 data set as the auxiliary data set, the recognition rate of the proposed framework is significantly improved on the UCF YouTube dataset.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences
Series
Advances in Computer Science Research
Publication Date
October 2015
ISBN
10.2991/iwmecs-15.2015.84
ISSN
2352-538X
DOI
10.2991/iwmecs-15.2015.84How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Bingbing Zhang
AU  - Dongcheng Shi
AU  - Kang Ni
AU  - Chao Liang
PY  - 2015/10
DA  - 2015/10
TI  - Learning Cross-domain Dictionary Pairs for Human Action Recognition
BT  - Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences
PB  - Atlantis Press
SP  - 419
EP  - 424
SN  - 2352-538X
UR  - https://doi.org/10.2991/iwmecs-15.2015.84
DO  - 10.2991/iwmecs-15.2015.84
ID  - Zhang2015/10
ER  -