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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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