Enhancing Effectiveness of Descriptors for Searching and Recognition 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%2F17%3A00094977" target="_blank" >RIV/00216224:14330/17:00094977 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ISM.2017.39" target="_blank" >http://dx.doi.org/10.1109/ISM.2017.39</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ISM.2017.39" target="_blank" >10.1109/ISM.2017.39</a>
Alternative languages
Result language
angličtina
Original language name
Enhancing Effectiveness of Descriptors for Searching and Recognition in Motion Capture Data
Original language description
Computer-aided analyses of motion capture data require an effective and efficient concept of motion similarity. Traditional methods generally compare motion sequences by applying time-warping techniques to high-dimensional trajectories of joints. An increasing effectiveness of machine-learning techniques, such as deep convolutional neural networks, brings new possibilities for similarity comparison. Inspired by recent advances in neural networks and image processing, we propose new variants of transformation of motion sequences into 2D images. The generated images are used to fine-tune a neural network from which 4,096D features are extracted and compared by a modified Euclidean distance. The proposed concept is not only efficient but also very effective and outperforms existing methods on a challenging dataset with 130 categories.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
19th IEEE International Symposium on Multimedia
ISBN
9781538629376
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
240-243
Publisher name
IEEE Computer Society
Place of publication
Neuveden
Event location
Taichung, Taiwan
Event date
Jan 1, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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