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@inproceedings{1390238,
author = {Sedmidubský, Jan and Eliáš, Petr and Zezula, Pavel},
address = {Neuveden},
booktitle = {19th IEEE International Symposium on Multimedia},
doi = {http://dx.doi.org/10.1109/ISM.2017.39},
keywords = {motion capture data; similarity-based comparison; motion images; joint weights; deep convolutional neural network; distance function; action recognition},
howpublished = {paměťový nosič},
language = {eng},
location = {Neuveden},
isbn = {978-1-5386-2937-6},
pages = {240-243},
publisher = {IEEE Computer Society},
title = {Enhancing Effectiveness of Descriptors for Searching and Recognition in Motion Capture Data},
year = {2017}
}
TY - JOUR
ID - 1390238
AU - Sedmidubský, Jan - Eliáš, Petr - Zezula, Pavel
PY - 2017
TI - Enhancing Effectiveness of Descriptors for Searching and Recognition in Motion Capture Data
PB - IEEE Computer Society
CY - Neuveden
SN - 9781538629376
KW - motion capture data
KW - similarity-based comparison
KW - motion images
KW - joint weights
KW - deep convolutional neural network
KW - distance function
KW - action recognition
N2 - Computer-aided analyses of motion capture data require an effective and efficient concept of motion similarity. Traditional methods generally compare motion sequences by applying time-warping techniques to high-dimensional trajectories of joints. An increasing effectiveness of machine-learning techniques, such as deep convolutional neural networks, brings new possibilities for similarity comparison. Inspired by recent advances in neural networks and image processing, we propose new variants of transformation of motion sequences into 2D images. The generated images are used to fine-tune a neural network from which 4,096D features are extracted and compared by a modified Euclidean distance. The proposed concept is not only efficient but also very effective and outperforms existing methods on a challenging dataset with 130 categories.
ER -
SEDMIDUBSKÝ, Jan, Petr ELIÁŠ and Pavel ZEZULA. Enhancing Effectiveness of Descriptors for Searching and Recognition in Motion Capture Data. In \textit{19th IEEE International Symposium on Multimedia}. Neuveden: IEEE Computer Society, 2017, p.~240-243. ISBN~978-1-5386-2937-6. Available from: https://dx.doi.org/10.1109/ISM.2017.39.
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