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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F13%3A00065724%21RIV14-MV0-14330___
rdf:type
n16:Vysledek skos:Concept
dcterms:description
Analysis of human motion data is an important task in many research fields such as sports, medicine, security, and computer animation. In order to fully exploit motion databases for further processing, effective and efficient retrieval methods are needed. However, such task is difficult primarily due to complex spatio-temporal variances of individual human motions and the rapidly increasing volume of motion data. In this paper, we propose a universal content-based subsequence retrieval algorithm for indexing and searching motion data. The algorithm is able to examine database motions and locate all their sub-motions that are similar to a query motion example. We illustrate the algorithm usability by indexing motion features in form of joint-angle rotations extracted from a real-life 68-minute human motion database. We analyse the algorithm time complexity and evaluate retrieval effectiveness by comparing the search results against user-defined ground truth. Analysis of human motion data is an important task in many research fields such as sports, medicine, security, and computer animation. In order to fully exploit motion databases for further processing, effective and efficient retrieval methods are needed. However, such task is difficult primarily due to complex spatio-temporal variances of individual human motions and the rapidly increasing volume of motion data. In this paper, we propose a universal content-based subsequence retrieval algorithm for indexing and searching motion data. The algorithm is able to examine database motions and locate all their sub-motions that are similar to a query motion example. We illustrate the algorithm usability by indexing motion features in form of joint-angle rotations extracted from a real-life 68-minute human motion database. We analyse the algorithm time complexity and evaluate retrieval effectiveness by comparing the search results against user-defined ground truth.
dcterms:title
A Key-Pose Similarity Algorithm for Motion Data Retrieval A Key-Pose Similarity Algorithm for Motion Data Retrieval
skos:prefLabel
A Key-Pose Similarity Algorithm for Motion Data Retrieval A Key-Pose Similarity Algorithm for Motion Data Retrieval
skos:notation
RIV/00216224:14330/13:00065724!RIV14-MV0-14330___
n16:predkladatel
n17:orjk%3A14330
n3:aktivita
n23:P
n3:aktivity
P(GBP103/12/G084), P(VG20122015073)
n3:dodaniDat
n5:2014
n3:domaciTvurceVysledku
n13:5763835 n13:3165647 n13:8731594
n3:druhVysledku
n19:D
n3:duvernostUdaju
n10:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
58669
n3:idVysledku
RIV/00216224:14330/13:00065724
n3:jazykVysledku
n14:eng
n3:klicovaSlova
motion capture data; motion retrieval; subsequence retrieval; similar sub-motions
n3:klicoveSlovo
n4:subsequence%20retrieval n4:similar%20sub-motions n4:motion%20capture%20data n4:motion%20retrieval
n3:kontrolniKodProRIV
[C2477BD56460]
n3:mistoKonaniAkce
Poznan, Poland
n3:mistoVydani
Switzerland
n3:nazevZdroje
Proceedings of 12th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2013), LNCS 8192
n3:obor
n15:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n12:GBP103%2F12%2FG084 n12:VG20122015073
n3:rokUplatneniVysledku
n5:2013
n3:tvurceVysledku
Zezula, Pavel Sedmidubský, Jan Valčík, Jakub
n3:typAkce
n6:WRD
n3:zahajeniAkce
2013-01-01+01:00
s:issn
0302-9743
s:numberOfPages
13
n7:doi
10.1007/978-3-319-02895-8_60
n11:hasPublisher
Springer International Publishing
n22:isbn
9783319028941
n20:organizacniJednotka
14330