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@inproceedings{1525476,
author = {Sedmidubský, Jan and Zezula, Pavel},
address = {New York, NY, USA},
booktitle = {International Conference on Multimedia Retrieval (ICMR)},
doi = {http://dx.doi.org/10.1145/3323873.3326922},
keywords = {3D skeleton sequence;action recognition;deep features;kNN},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York, NY, USA},
isbn = {978-1-4503-6765-3},
pages = {395-398},
publisher = {ACM},
title = {Recognizing User-Defined Subsequences in Human Motion Data},
year = {2019}
}
TY - JOUR
ID - 1525476
AU - Sedmidubský, Jan - Zezula, Pavel
PY - 2019
TI - Recognizing User-Defined Subsequences in Human Motion Data
PB - ACM
CY - New York, NY, USA
SN - 9781450367653
KW - 3D skeleton sequence;action recognition;deep features;kNN
N2 - Motion capture technologies digitize human movements by tracking 3D positions of specific skeleton joints in time. Such spatio-temporal multimedia data have an enormous application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. In this paper, we focus on an important task of recognition of a user-defined motion, based on a collection of labelled actions known in advance. We utilize current advances in deep feature learning and scalable similarity retrieval to build an effective and efficient k-nearest-neighbor recognition technique for 3D human motion data. The properties of the technique are demonstrated by a web application which allows a user to browse long motion sequences and specify any subsequence as the input for probabilistic recognition based on 130 predefined classes.
ER -
SEDMIDUBSKÝ, Jan and Pavel ZEZULA. Recognizing User-Defined Subsequences in Human Motion Data. Online. In \textit{International Conference on Multimedia Retrieval (ICMR)}. New York, NY, USA: ACM, 2019, p.~395-398. ISBN~978-1-4503-6765-3. Available from: https://dx.doi.org/10.1145/3323873.3326922.
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