Effective and Efficient Similarity Searching in Motion Capture Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00100703" target="_blank" >RIV/00216224:14330/18:00100703 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/s11042-017-4859-7" target="_blank" >http://dx.doi.org/10.1007/s11042-017-4859-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11042-017-4859-7" target="_blank" >10.1007/s11042-017-4859-7</a>
Alternative languages
Result language
angličtina
Original language name
Effective and Efficient Similarity Searching in Motion Capture Data
Original language description
Motion capture data describe human movements in the form of spatio-temporal trajectories of skeleton joints. Intelligent management of such complex data is a challenging task for computers which requires an effective concept of motion similarity. However, evaluating the pair-wise similarity is a difficult problem as a single action can be performed by various actors in different ways, speeds or starting positions. Recent methods usually model the motion similarity by comparing customized features using distance-based functions or specialized machine-learning classifiers. By combining both these approaches, we transform the problem of comparing motions of variable sizes into the problem of comparing fixed-size vectors. Specifically, each rather-short motion is encoded into a compact visual representation from which a highly descriptive 4,096-dimensional feature vector is extracted using a fine-tuned deep convolutional neural network. The advantage is that the fixed-size features are compared by the Euclidean distance which enables efficient motion indexing by any metric-based index structure. Another advantage of the proposed approach is its tolerance towards an imprecise action segmentation, the variance in movement speed, and a lower data quality. All these properties together bring new possibilities for effective and efficient large-scale retrieval.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Multimedia Tools and Applications
ISSN
1380-7501
e-ISSN
1573-7721
Volume of the periodical
77
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
22
Pages from-to
12073-12094
UT code for WoS article
000433202100021
EID of the result in the Scopus database
2-s2.0-85019711344