Assessing similarity models for human-motion retrieval
applications

J 2016

Assessing similarity models for human-motion retrieval applications

VALČÍK, Jakub, Jan SEDMIDUBSKÝ a Pavel ZEZULA

Základní údaje

Originální název

Assessing similarity models for human-motion retrieval applications

Autoři

VALČÍK, Jakub (203 Česká republika, domácí), Jan SEDMIDUBSKÝ (203 Česká republika, garant, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

Computer Animation and Virtual Worlds, John Wiley & Sons Ltd, 2016, 1546-4261

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 0.424

Kód RIV

RIV/00216224:14330/16:00087724

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1002/cav.1674

UT WoS

000385614100003

Klíčová slova anglicky

human-motion retrieval; similarity model; effectiveness evaluation; motion capture data; action recognition

Štítky

best, DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 4. 2019 07:36, doc. RNDr. Jan Sedmidubský, Ph.D.

Anotace

V originále

The development of motion capturing devices poses new challenges in the exploitation of human-motion data for various application fields, such as computer animation, visual surveillance, sports or physical medicine. Recently, a number of approaches dealing with motion data have been proposed, suggesting characteristic motion features to be extracted and compared on the basis of similarity. Unfortunately, almost each approach defines its own set of motion features and comparison methods, thus it is hard to fairly decide which similarity model is the most suitable for a given kind of human-motion retrieval application. To cope with this problem, we propose the HumAn Motion Model EvaluatoR (HAMMER) which is a generic framework for assessing candidate similarity models with respect to the purpose of the target application. The application purpose is specified by a user in form of a representative sample of categorized motion data. Respecting such categorization, the similarity models are assessed from the effectiveness and efficiency points of view using a set of space-complexity, information-retrieval, and performance measures. The usability of the framework is demonstrated by case studies of three practical examples of retrieval applications focusing on recognition of actions, detection of similar events, and identification of subjects.

Návaznosti

GBP103/12/G084, projekt VaV

Název: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Grantová agentura ČR, Centrum pro multi-modální interpretaci dat velkého rozsahu

Citovat

VALČÍK, Jakub, Jan SEDMIDUBSKÝ a Pavel ZEZULA. Assessing similarity models for human-motion retrieval applications. Computer Animation and Virtual Worlds. John Wiley & Sons Ltd, 2016, roč. 27, č. 5, s. 484-500. ISSN 1546-4261. Dostupné z: https://dx.doi.org/10.1002/cav.1674.

@article{1305925,
   author = {Valčík, Jakub and Sedmidubský, Jan and Zezula, Pavel},
   article_number = {5},
   doi = {http://dx.doi.org/10.1002/cav.1674},
   keywords = {human-motion retrieval; similarity model; effectiveness evaluation; motion capture data; action recognition},
   language = {eng},
   issn = {1546-4261},
   journal = {Computer Animation and Virtual Worlds},
   title = {Assessing similarity models for human-motion retrieval applications},
   volume = {27},
   year = {2016}
}

TY  - JOUR
ID  - 1305925
AU  - Valčík, Jakub - Sedmidubský, Jan - Zezula, Pavel
PY  - 2016
TI  - Assessing similarity models for human-motion retrieval applications
JF  - Computer Animation and Virtual Worlds
VL  - 27
IS  - 5
SP  - 484-500
EP  - 484-500
PB  - John Wiley & Sons Ltd
SN  - 15464261
KW  - human-motion retrieval
KW  - similarity model
KW  - effectiveness evaluation
KW  - motion capture data
KW  - action recognition
N2  - The development of motion capturing devices poses new challenges in the exploitation of human-motion data for various application fields, such as computer animation, visual surveillance, sports or physical medicine. Recently, a number of approaches dealing with motion data have been proposed, suggesting characteristic motion features to be extracted and compared on the basis of similarity. Unfortunately, almost each approach defines its own set of motion features and comparison methods, thus it is hard to fairly decide which similarity model is the most suitable for a given kind of human-motion retrieval application. To cope with this problem, we propose the HumAn Motion Model EvaluatoR (HAMMER) which is a generic framework for assessing candidate similarity models with respect to the purpose of the target application. The application purpose is specified by a user in form of a representative sample of categorized motion data. Respecting such categorization, the similarity models are assessed from the effectiveness and efficiency points of view using a set of space-complexity, information-retrieval, and performance measures. The usability of the framework is demonstrated by case studies of three practical examples of retrieval applications focusing on recognition of actions, detection of similar events, and identification of subjects.
ER  -

VALČÍK, Jakub, Jan SEDMIDUBSKÝ a Pavel ZEZULA. Assessing similarity models for human-motion retrieval applications. \textit{Computer Animation and Virtual Worlds}. John Wiley \&{} Sons Ltd, 2016, roč.~27, č.~5, s.~484-500. ISSN~1546-4261. Dostupné z: https://dx.doi.org/10.1002/cav.1674.

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