Content-Based Management of Human Motion Data: Survey and
Challenges

J 2021

Content-Based Management of Human Motion Data: Survey and Challenges

SEDMIDUBSKÝ, Jan, Petr ELIÁŠ, Petra BUDÍKOVÁ a Pavel ZEZULA

Základní údaje

Originální název

Content-Based Management of Human Motion Data: Survey and Challenges

Autoři

SEDMIDUBSKÝ, Jan (203 Česká republika, garant, domácí), Petr ELIÁŠ (203 Česká republika, domácí), Petra BUDÍKOVÁ (203 Česká republika, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

IEEE Access, IEEE Xplore Digital Library, 2021, 2169-3536

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.476

Kód RIV

RIV/00216224:14330/21:00119002

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1109/ACCESS.2021.3075766

UT WoS

000645842300001

Klíčová slova anglicky

Action detection; content-based processing; deep features; metric learning; motion capture data; skeleton sequences; similarity; sub-sequence search

Štítky

DISA

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 20. 4. 2022 10:54, doc. RNDr. Jan Sedmidubský, Ph.D.

Anotace

V originále

Digitization of human motion using skeleton representations offers exciting possibilities for a large number of applications but, at the same time, requires innovative techniques for their effective and efficient processing. Content-based processing of skeleton data has developed rapidly in recent years, focusing mainly on specialized prototypes with limited consideration of generic data management possibilities. In this survey article, we synthesize and categorize the existing approaches and outline future research challenges brought by the increasing availability of human motion data. In particular, we first discuss the problems of suitable representation and segmentation of continuous skeleton data obtained from various sources. Then, we concentrate on comparison models for assessing the similarity of time-restricted pieces of motions, as required by any content-based management operation. Next, we review the techniques for evaluating similarity queries over collections of motion sequences and filtering query-relevant parts from continuous motion streams. Finally, we summarize the usability of existing techniques in perspective application domains and discuss the new challenges related to current technological and infrastructural developments. We especially assess the existing techniques from the perspective of scalability and propose future research directions for dealing with large and diverse volumes of skeleton data.

Návaznosti

GA19-02033S, projekt VaV

Název: Vyhledávání, analytika a anotace datových toků lidských pohybů

Investor: Grantová agentura ČR, Searching, Mining, and Annotating Human Motion Streams

Citovat

SEDMIDUBSKÝ, Jan, Petr ELIÁŠ, Petra BUDÍKOVÁ a Pavel ZEZULA. Content-Based Management of Human Motion Data: Survey and Challenges. IEEE Access. IEEE Xplore Digital Library, 2021, roč. 9, 26 April 2021, s. 64241-64255. ISSN 2169-3536. Dostupné z: https://dx.doi.org/10.1109/ACCESS.2021.3075766.

@article{1773602,
   author = {Sedmidubský, Jan and Eliáš, Petr and Budíková, Petra and Zezula, Pavel},
   article_number = {26 April 2021},
   doi = {http://dx.doi.org/10.1109/ACCESS.2021.3075766},
   keywords = {Action detection; content-based processing; deep features; metric learning; motion capture data; skeleton sequences; similarity; sub-sequence search},
   language = {eng},
   issn = {2169-3536},
   journal = {IEEE Access},
   title = {Content-Based Management of Human Motion Data: Survey and Challenges},
   url = {https://ieeexplore.ieee.org/document/9416451},
   volume = {9},
   year = {2021}
}

TY  - JOUR
ID  - 1773602
AU  - Sedmidubský, Jan - Eliáš, Petr - Budíková, Petra - Zezula, Pavel
PY  - 2021
TI  - Content-Based Management of Human Motion Data: Survey and Challenges
JF  - IEEE Access
VL  - 9
IS  - 26 April 2021
SP  - 64241-64255
EP  - 64241-64255
PB  - IEEE Xplore Digital Library
SN  - 21693536
KW  - Action detection
KW  - content-based processing
KW  - deep features
KW  - metric learning
KW  - motion capture data
KW  - skeleton sequences
KW  - similarity
KW  - sub-sequence search
UR  - https://ieeexplore.ieee.org/document/9416451
N2  - Digitization of human motion using skeleton representations offers exciting possibilities for a large number of applications but, at the same time, requires innovative techniques for their effective and efficient processing. Content-based processing of skeleton data has developed rapidly in recent years, focusing mainly on specialized prototypes with limited consideration of generic data management possibilities. In this survey article, we synthesize and categorize the existing approaches and outline future research challenges brought by the increasing availability of human motion data. In particular, we first discuss the problems of suitable representation and segmentation of continuous skeleton data obtained from various sources. Then, we concentrate on comparison models for assessing the similarity of time-restricted pieces of motions, as required by any content-based management operation. Next, we review the techniques for evaluating similarity queries over collections of motion sequences and filtering query-relevant parts from continuous motion streams. Finally, we summarize the usability of existing techniques in perspective application domains and discuss the new challenges related to current technological and infrastructural developments. We especially assess the existing techniques from the perspective of scalability and propose future research directions for dealing with large and diverse volumes of skeleton data.
ER  -

SEDMIDUBSKÝ, Jan, Petr ELIÁŠ, Petra BUDÍKOVÁ a Pavel ZEZULA. Content-Based Management of Human Motion Data: Survey and Challenges. \textit{IEEE Access}. IEEE Xplore Digital Library, 2021, roč.~9, 26 April 2021, s.~64241-64255. ISSN~2169-3536. Dostupné z: https://dx.doi.org/10.1109/ACCESS.2021.3075766.

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