Recognizing User-Defined Subsequences in Human Motion Data

D 2019

Recognizing User-Defined Subsequences in Human Motion Data

SEDMIDUBSKÝ, Jan and Pavel ZEZULA

Basic information

Original name

Recognizing User-Defined Subsequences in Human Motion Data

Authors

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

Edition

New York, NY, USA, International Conference on Multimedia Retrieval (ICMR), p. 395-398, 4 pp. 2019

Publisher

ACM

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/00216224:14330/19:00107370

Organization unit

Faculty of Informatics

ISBN

978-1-4503-6765-3

DOI

http://dx.doi.org/10.1145/3323873.3326922

UT WoS

000482188900058

Keywords in English

3D skeleton sequence;action recognition;deep features;kNN

Abstract

V originále

Motion capture technologies digitize human movements by tracking 3D positions of specific skeleton joints in time. Such spatio-temporal multimedia data have an enormous application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. In this paper, we focus on an important task of recognition of a user-defined motion, based on a collection of labelled actions known in advance. We utilize current advances in deep feature learning and scalable similarity retrieval to build an effective and efficient k-nearest-neighbor recognition technique for 3D human motion data. The properties of the technique are demonstrated by a web application which allows a user to browse long motion sequences and specify any subsequence as the input for probabilistic recognition based on 130 predefined classes.

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. Recognizing User-Defined Subsequences in Human Motion Data. Online. In International Conference on Multimedia Retrieval (ICMR). New York, NY, USA: ACM, 2019, p. 395-398. ISBN 978-1-4503-6765-3. Available from: https://dx.doi.org/10.1145/3323873.3326922.

@inproceedings{1525476,
   author = {Sedmidubský, Jan and Zezula, Pavel},
   address = {New York, NY, USA},
   booktitle = {International Conference on Multimedia Retrieval (ICMR)},
   doi = {http://dx.doi.org/10.1145/3323873.3326922},
   keywords = {3D skeleton sequence;action recognition;deep features;kNN},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, USA},
   isbn = {978-1-4503-6765-3},
   pages = {395-398},
   publisher = {ACM},
   title = {Recognizing User-Defined Subsequences in Human Motion Data},
   year = {2019}
}

TY  - JOUR
ID  - 1525476
AU  - Sedmidubský, Jan - Zezula, Pavel
PY  - 2019
TI  - Recognizing User-Defined Subsequences in Human Motion Data
PB  - ACM
CY  - New York, NY, USA
SN  - 9781450367653
KW  - 3D skeleton sequence;action recognition;deep features;kNN
N2  - Motion capture technologies digitize human movements by tracking 3D positions of specific skeleton joints in time. Such spatio-temporal multimedia data have an enormous application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. In this paper, we focus on an important task of recognition of a user-defined motion, based on a collection of labelled actions known in advance. We utilize current advances in deep feature learning and scalable similarity retrieval to build an effective and efficient k-nearest-neighbor recognition technique for 3D human motion data. The properties of the technique are demonstrated by a web application which allows a user to browse long motion sequences and specify any subsequence as the input for probabilistic recognition based on 130 predefined classes.
ER  -

SEDMIDUBSKÝ, Jan and Pavel ZEZULA. Recognizing User-Defined Subsequences in Human Motion Data. Online. In \textit{International Conference on Multimedia Retrieval (ICMR)}. New York, NY, USA: ACM, 2019, p.~395-398. ISBN~978-1-4503-6765-3. Available from: https://dx.doi.org/10.1145/3323873.3326922.

Detailed Information on Publication Record