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@article{1381296,
author = {Sedmidubský, Jan and Eliáš, Petr and Zezula, Pavel},
article_number = {10},
doi = {http://dx.doi.org/10.1007/s11042-017-4859-7},
keywords = {Motion capture data retrieval;Effective similarity measure;Efficient indexing;k-NN query;Motion image;Convolutional neural network;Fixed-size motion feature},
language = {eng},
issn = {1380-7501},
journal = {Multimedia Tools and Applications},
title = {Effective and Efficient Similarity Searching in Motion Capture Data},
volume = {77},
year = {2018}
}
TY - JOUR
ID - 1381296
AU - Sedmidubský, Jan - Eliáš, Petr - Zezula, Pavel
PY - 2018
TI - Effective and Efficient Similarity Searching in Motion Capture Data
JF - Multimedia Tools and Applications
VL - 77
IS - 10
SP - 12073-12094
EP - 12073-12094
PB - Springer US
SN - 13807501
KW - Motion capture data retrieval;Effective similarity measure;Efficient indexing;k-NN query;Motion image;Convolutional neural network;Fixed-size motion feature
N2 - Motion capture data describe human movements in the form of spatio-temporal trajectories of skeleton joints. Intelligent management of such complex data is a challenging task for computers which requires an effective concept of motion similarity. However, evaluating the pair-wise similarity is a difficult problem as a single action can be performed by various actors in different ways, speeds or starting positions. Recent methods usually model the motion similarity by comparing customized features using distance-based functions or specialized machine-learning classifiers. By combining both these approaches, we transform the problem of comparing motions of variable sizes into the problem of comparing fixed-size vectors. Specifically, each rather-short motion is encoded into a compact visual representation from which a highly descriptive 4,096-dimensional feature vector is extracted using a fine-tuned deep convolutional neural network. The advantage is that the fixed-size features are compared by the Euclidean distance which enables efficient motion indexing by any metric-based index structure. Another advantage of the proposed approach is its tolerance towards an imprecise action segmentation, the variance in movement speed, and a lower data quality. All these properties together bring new possibilities for effective and efficient large-scale retrieval.
ER -
SEDMIDUBSKÝ, Jan, Petr ELIÁŠ a Pavel ZEZULA. Effective and Efficient Similarity Searching in Motion Capture Data. \textit{Multimedia Tools and Applications}. Springer US, 2018, roč.~77, č.~10, s.~12073-12094. ISSN~1380-7501. Dostupné z: https://dx.doi.org/10.1007/s11042-017-4859-7.
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