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@inproceedings{1684078,
author = {Budíková, Petra and Sedmidubský, Jan and Horváth, Ján and Zezula, Pavel},
address = {Washington, DC},
booktitle = {22nd IEEE International Symposium on Multimedia (ISM)},
doi = {http://dx.doi.org/10.1109/ISM.2020.00015},
keywords = {skeleton data; human motion retrieval; motion episodes; text-based processing},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Washington, DC},
isbn = {978-1-7281-8697-9},
pages = {49-56},
publisher = {IEEE Computer Society},
title = {Towards Scalable Retrieval of Human Motion Episodes},
year = {2020}
}
TY - JOUR
ID - 1684078
AU - Budíková, Petra - Sedmidubský, Jan - Horváth, Ján - Zezula, Pavel
PY - 2020
TI - Towards Scalable Retrieval of Human Motion Episodes
PB - IEEE Computer Society
CY - Washington, DC
SN - 9781728186979
KW - skeleton data
KW - human motion retrieval
KW - motion episodes
KW - text-based processing
N2 - With the increasing availability of human motion data captured in the form of 2D/3D skeleton sequences, more complex motion recordings need to be processed. In this paper, we study the problem of similarity-based matching of medium-sized unsegmented skeleton sequences, which we denote as motion episodes. We first apply standard pose-based approaches for matching episodes and analyze their shortcomings. Then, we adopt a recent segment-based approach that transforms episode data into a text-like representation, and apply mature text-processing techniques for matching episodes. We demonstrate that this text-based approach achieves promising results in the terms of both effectiveness and efficiency, and can be further indexed to implement scalable episode retrieval.
ER -
BUDÍKOVÁ, Petra, Jan SEDMIDUBSKÝ, Ján HORVÁTH a Pavel ZEZULA. Towards Scalable Retrieval of Human Motion Episodes. Online. In \textit{22nd IEEE International Symposium on Multimedia (ISM)}. Washington, DC: IEEE Computer Society, 2020, s.~49-56. ISBN~978-1-7281-8697-9. Dostupné z: https://dx.doi.org/10.1109/ISM.2020.00015.
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