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@inproceedings{1525456,
author = {Sedmidubský, Jan and Zezula, Pavel},
address = {New York, NY, USA},
booktitle = {International Conference on Multimedia Retrieval (ICMR)},
doi = {http://dx.doi.org/10.1145/3323873.3326589},
keywords = {motion capture data;3D skeleton sequence;similarity search;subsequence matching;annotation;action detection;stream processing},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York, NY, USA},
isbn = {978-1-4503-6765-3},
pages = {5-6},
publisher = {ACM},
title = {Similarity Search in 3D Human Motion Data},
year = {2019}
}
TY - JOUR
ID - 1525456
AU - Sedmidubský, Jan - Zezula, Pavel
PY - 2019
TI - Similarity Search in 3D Human Motion Data
PB - ACM
CY - New York, NY, USA
SN - 9781450367653
KW - motion capture data;3D skeleton sequence;similarity search;subsequence matching;annotation;action detection;stream processing
N2 - Motion capture technologies can digitize human movements into a discrete sequence of 3D skeletons. Such spatio-temporal data have a great application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. The objective of this tutorial is to explain fundamental principles and technologies designed for searching, subsequence matching, classification and action detection in the 3D human motion data. These operations inherently require the concept of similarity to determine the degree of accordance between pairs of 3D skeleton sequences. Such similarity can be modeled using a generic approach of metric space by extracting effective deep features and comparing them by efficient distance functions. The metric-space approach also enables applying traditional index structures to efficiently access large datasets of skeleton sequences. We demonstrate the functionality of selected motion-processing operations by interactive web applications.
ER -
SEDMIDUBSKÝ, Jan a Pavel ZEZULA. Similarity Search in 3D Human Motion Data. Online. In \textit{International Conference on Multimedia Retrieval (ICMR)}. New York, NY, USA: ACM, 2019, s.~5-6. ISBN~978-1-4503-6765-3. Dostupné z: https://dx.doi.org/10.1145/3323873.3326589.
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