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@inproceedings{1349484,
author = {Sedmidubský, Jan and Eliáš, Petr and Zezula, Pavel},
address = {Cham (ZG)},
booktitle = {Proceedings of 9th International Conference on Similarity Search and Applications (SISAP 2016), LNCS 9939},
doi = {http://dx.doi.org/10.1007/978-3-319-46759-7_21},
editor = {L. Amsaleg et al.},
keywords = {motion capture data; similarity search; subsequence search; multi-level segmentation},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Cham (ZG)},
isbn = {978-3-319-46758-0},
pages = {271-285},
publisher = {Springer International Publishing AG},
title = {Similarity Searching in Long Sequences of Motion Capture Data},
year = {2016}
}
TY - JOUR
ID - 1349484
AU - Sedmidubský, Jan - Eliáš, Petr - Zezula, Pavel
PY - 2016
TI - Similarity Searching in Long Sequences of Motion Capture Data
PB - Springer International Publishing AG
CY - Cham (ZG)
SN - 9783319467580
KW - motion capture data
KW - similarity search
KW - subsequence search
KW - multi-level segmentation
N2 - Motion capture data digitally represent human movements by sequences of body configurations in time. Searching in such spatio-temporal data is difficult as query-relevant motions can vary in lengths and occur arbitrarily in the very long data sequence. There is also a strong requirement on effective similarity comparison as the specific motion can be performed by various actors in different ways, speeds or starting positions. To deal with these problems, we propose a new subsequence matching algorithm which uses a synergy of elastic similarity measure and multi-level segmentation. The idea is to generate a minimum number of overlapping data segments so that there is at least one segment matching an arbitrary subsequence. A non-partitioned query is then efficiently evaluated by searching for the most similar segments in a single level only, while guaranteeing a precise answer with respect to the similarity measure. The retrieval process is efficient and scalable which is confirmed by experiments executed on a real-life dataset.
ER -
SEDMIDUBSKÝ, Jan, Petr ELIÁŠ and Pavel ZEZULA. Similarity Searching in Long Sequences of Motion Capture Data. In L. Amsaleg et al. \textit{Proceedings of 9th International Conference on Similarity Search and Applications (SISAP 2016), LNCS 9939}. Cham (ZG): Springer International Publishing AG, 2016, p.~271-285. ISBN~978-3-319-46758-0. Available from: https://dx.doi.org/10.1007/978-3-319-46759-7\_{}21.
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