Content-Based Management of Human Motion Data: Survey and Challenges
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00119002" target="_blank" >RIV/00216224:14330/21:00119002 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9416451" target="_blank" >https://ieeexplore.ieee.org/document/9416451</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2021.3075766" target="_blank" >10.1109/ACCESS.2021.3075766</a>
Alternative languages
Result language
angličtina
Original language name
Content-Based Management of Human Motion Data: Survey and Challenges
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/GA19-02033S" target="_blank" >GA19-02033S: Searching, Mining, and Annotating Human Motion Streams</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
9
Issue of the periodical within the volume
26 April 2021
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
64241-64255
UT code for WoS article
000645842300001
EID of the result in the Scopus database
2-s2.0-85107184215