Augmenting Spatio-Temporal Human Motion Data for Effective 3D
Action Recognition

D 2019

Augmenting Spatio-Temporal Human Motion Data for Effective 3D Action Recognition

SEDMIDUBSKÝ, Jan and Pavel ZEZULA

Basic information

Original name

Augmenting Spatio-Temporal Human Motion Data for Effective 3D Action Recognition

Authors

SEDMIDUBSKÝ, Jan (203 Czech Republic, guarantor, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

Neuveden, 21st IEEE International Symposium on Multimedia (ISM), p. 204-207, 4 pp. 2019

Publisher

IEEE Computer Society

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/19:00107708

Organization unit

Faculty of Informatics

ISBN

978-1-72815-606-4

DOI

http://dx.doi.org/10.1109/ISM46123.2019.00044

UT WoS

000528909200033

Keywords in English

3D skeleton sequence;multimedia data;data augmentation;action recognition;bidirectional LSTM

Abstract

V originále

Action recognition is a fundamental operation in 3D human motion analysis. Existing deep learning classifiers achieve a high recognition accuracy if large amounts of training data are provided. However, such data are difficult to obtain in a variety of application scenarios, mainly due to the high costs of motion capture technologies and an absence of suitable actors. In this paper, we propose augmentation techniques to artificially enlarge existing collections of 3D human skeleton sequences. The proposed techniques are especially useful for datasets distinguishing in a high number of classes, each of them characterized by only a limited number of action samples. We experimentally demonstrate that the augmented data help to significantly increase the recognition accuracy even using a standard deep learning architecture.

Links

GA19-02033S, research and development project

Name: Vyhledávání, analytika a anotace datových toků lidských pohybů

Investor: Czech Science Foundation

Citovat

SEDMIDUBSKÝ, Jan and Pavel ZEZULA. Augmenting Spatio-Temporal Human Motion Data for Effective 3D Action Recognition. Online. In 21st IEEE International Symposium on Multimedia (ISM). Neuveden: IEEE Computer Society, 2019, p. 204-207. ISBN 978-1-72815-606-4. Available from: https://dx.doi.org/10.1109/ISM46123.2019.00044.

@inproceedings{1566957,
   author = {Sedmidubský, Jan and Zezula, Pavel},
   address = {Neuveden},
   booktitle = {21st IEEE International Symposium on Multimedia (ISM)},
   doi = {http://dx.doi.org/10.1109/ISM46123.2019.00044},
   keywords = {3D skeleton sequence;multimedia data;data augmentation;action recognition;bidirectional LSTM},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Neuveden},
   isbn = {978-1-72815-606-4},
   pages = {204-207},
   publisher = {IEEE Computer Society},
   title = {Augmenting Spatio-Temporal Human Motion Data for Effective 3D Action Recognition},
   year = {2019}
}

TY  - JOUR
ID  - 1566957
AU  - Sedmidubský, Jan - Zezula, Pavel
PY  - 2019
TI  - Augmenting Spatio-Temporal Human Motion Data for Effective 3D Action Recognition
PB  - IEEE Computer Society
CY  - Neuveden
SN  - 9781728156064
KW  - 3D skeleton sequence;multimedia data;data augmentation;action recognition;bidirectional LSTM
N2  - Action recognition is a fundamental operation in 3D human motion analysis. Existing deep learning classifiers achieve a high recognition accuracy if large amounts of training data are provided. However, such data are difficult to obtain in a variety of application scenarios, mainly due to the high costs of motion capture technologies and an absence of suitable actors. In this paper, we propose augmentation techniques to artificially enlarge existing collections of 3D human skeleton sequences. The proposed techniques are especially useful for datasets distinguishing in a high number of classes, each of them characterized by only a limited number of action samples. We experimentally demonstrate that the augmented data help to significantly increase the recognition accuracy even using a standard deep learning architecture.
ER  -

SEDMIDUBSKÝ, Jan and Pavel ZEZULA. Augmenting Spatio-Temporal Human Motion Data for Effective 3D Action Recognition. Online. In \textit{21st IEEE International Symposium on Multimedia (ISM)}. Neuveden: IEEE Computer Society, 2019, p.~204-207. ISBN~978-1-72815-606-4. Available from: https://dx.doi.org/10.1109/ISM46123.2019.00044.

Detailed Information on Publication Record