SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00130177" target="_blank" >RIV/00216224:14330/23:00130177 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-28238-6_8" target="_blank" >http://dx.doi.org/10.1007/978-3-031-28238-6_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-28238-6_8" target="_blank" >10.1007/978-3-031-28238-6_8</a>

Result language
angličtina
Original language name
SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval
Original language description
Recent progress in pose-estimation methods enables the extraction of sufficiently-precise 3D human skeleton data from ordinary videos, which offers great opportunities for a wide range of applications. However, such spatio-temporal data are typically extracted in the form of a continuous skeleton sequence without any information about semantic segmentation or annotation. To make the extracted data reusable for further processing, there is a need to access them based on their content. In this paper, we introduce a universal retrieval approach that compares any two skeleton sequences based on temporal order and similarities of their underlying segments. The similarity of segments is determined by their content-preserving low-dimensional code representation that is learned using the Variational AutoEncoder principle in an unsupervised way. The quality of the proposed representation is validated in retrieval and classification scenarios; our proposal outperforms the state-of-the-art approaches in effectiveness and reaches speed-ups up to 64x on common skeleton sequence datasets.
Czech name
—
Czech description
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Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
45th European Conference on Information Retrieval (ECIR)
ISBN
9783031282379
ISSN
0302-9743
e-ISSN
—
Number of pages
15
Pages from-to
110-124
Publisher name
Springer
Place of publication
Cham
Event location
Dublin, Ireland
Event date
Jan 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000995489700008

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