Semantically Consistent Human Motion Segmentation

D 2014

Semantically Consistent Human Motion Segmentation

BALÁŽIA, Michal, Jan SEDMIDUBSKÝ and Pavel ZEZULA

Basic information

Original name

Semantically Consistent Human Motion Segmentation

Authors

BALÁŽIA, Michal (703 Slovakia, belonging to the institution), Jan SEDMIDUBSKÝ (203 Czech Republic, guarantor, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

LNCS 8644. Switzerland, Proceedings of 25th International Conference on Database and Expert Systems Applications (DEXA 2014), p. 423-437, 15 pp. 2014

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/14:00073223

Organization unit

Faculty of Informatics

ISBN

978-3-319-10072-2

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-10073-9_36

Keywords in English

motion capture data; segmentation; semantic consistency; phases of movement; motion retrieval

Abstract

V originále

The development of motion capturing devices like Microsoft Kinect poses new challenges in the exploitation of human-motion data for various application fields, such as computer animation, visual surveillance, sports or physical medicine. In such applications, motion segmentation is recognized as one of the most fundamental steps. Existing methods usually segment motions at the level of logical actions, like walking or jumping, to annotate the motion segments by textual descriptions. Although the action-level segmentation is convenient for motion summarization and action retrieval, it does not suit for general action-independent motion retrieval. In this paper, we introduce a novel semantically consistent algorithm for partitioning motions into short and further non-divisible segments. The property of semantic consistency ensures that the start and end of each segment are detected at semantically equivalent phases of movement to support general motion retrieval. The proposed segmentation algorithm first extracts relative distances between particular body parts as motion features. Based on these features, segments are consequently identified by constructing and analyzing a one-dimensional energy curve representing local motion changes. Experiments conducted on real-life motions demonstrate that the algorithm outperforms other relevant approaches in terms of recall and precision with respect to a user-defined ground truth. Moreover, it identifies segments at semantically equivalent phases with the highest accuracy.

Links

GBP103/12/G084, research and development project

Name: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Czech Science Foundation

MUNI/A/0915/2013, interní kód MU

Name: Výzkum FI ve vybraných oblastech aplikované informatiky (Acronym: FI_Apl_Inf_2014)

Investor: Masaryk University, Category A

VG20122015073, research and development project

Name: Efektivní vyhledávání v rozsáhlých biometrických datech (Acronym: EFBIO)

Investor: Ministry of the Interior of the CR

Citovat

BALÁŽIA, Michal, Jan SEDMIDUBSKÝ and Pavel ZEZULA. Semantically Consistent Human Motion Segmentation. In Hendrik Decker, Lenka Lhotská, Sebastian Link, Marcus Spies, Roland R. Wagner. Proceedings of 25th International Conference on Database and Expert Systems Applications (DEXA 2014). LNCS 8644. Switzerland: Springer, 2014, p. 423-437. ISBN 978-3-319-10072-2. Available from: https://dx.doi.org/10.1007/978-3-319-10073-9_36.

@inproceedings{1183912,
   author = {Balážia, Michal and Sedmidubský, Jan and Zezula, Pavel},
   address = {Switzerland},
   booktitle = {Proceedings of 25th International Conference on Database and Expert Systems Applications (DEXA 2014)},
   doi = {http://dx.doi.org/10.1007/978-3-319-10073-9_36},
   edition = {LNCS 8644},
   editor = {Hendrik Decker, Lenka Lhotská, Sebastian Link, Marcus Spies, Roland R. Wagner},
   keywords = {motion capture data; segmentation; semantic consistency; phases of movement; motion retrieval},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Switzerland},
   isbn = {978-3-319-10072-2},
   pages = {423-437},
   publisher = {Springer},
   title = {Semantically Consistent Human Motion Segmentation},
   year = {2014}
}

TY  - JOUR
ID  - 1183912
AU  - Balážia, Michal - Sedmidubský, Jan - Zezula, Pavel
PY  - 2014
TI  - Semantically Consistent Human Motion Segmentation
PB  - Springer
CY  - Switzerland
SN  - 9783319100722
KW  - motion capture data
KW  - segmentation
KW  - semantic consistency
KW  - phases of movement
KW  - motion retrieval
N2  - The development of motion capturing devices like Microsoft Kinect poses new challenges in the exploitation of human-motion data for various application fields, such as computer animation, visual surveillance, sports or physical medicine. In such applications, motion segmentation is recognized as one of the most fundamental steps. Existing methods usually segment motions at the level of logical actions, like walking or jumping, to annotate the motion segments by textual descriptions. Although the action-level segmentation is convenient for motion summarization and action retrieval, it does not suit for general action-independent motion retrieval. In this paper, we introduce a novel semantically consistent algorithm for partitioning motions into short and further non-divisible segments. The property of semantic consistency ensures that the start and end of each segment are detected at semantically equivalent phases of movement to support general motion retrieval. The proposed segmentation algorithm first extracts relative distances between particular body parts as motion features. Based on these features, segments are consequently identified by constructing and analyzing a one-dimensional energy curve representing local motion changes. Experiments conducted on real-life motions demonstrate that the algorithm outperforms other relevant approaches in terms of recall and precision with respect to a user-defined ground truth. Moreover, it identifies segments at semantically equivalent phases with the highest accuracy.
ER  -

BALÁŽIA, Michal, Jan SEDMIDUBSKÝ and Pavel ZEZULA. Semantically Consistent Human Motion Segmentation. In Hendrik Decker, Lenka Lhotská, Sebastian Link, Marcus Spies, Roland R. Wagner. \textit{Proceedings of 25th International Conference on Database and Expert Systems Applications (DEXA 2014)}. LNCS 8644. Switzerland: Springer, 2014, p.~423-437. ISBN~978-3-319-10072-2. Available from: https://dx.doi.org/10.1007/978-3-319-10073-9\_{}36.

Detailed Information on Publication Record