Probabilistic Classification of Skeleton Sequences

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00100948" target="_blank" >RIV/00216224:14330/18:00100948 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-98812-2_4" target="_blank" >http://dx.doi.org/10.1007/978-3-319-98812-2_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-98812-2_4" target="_blank" >10.1007/978-3-319-98812-2_4</a>

Result language
angličtina
Original language name
Probabilistic Classification of Skeleton Sequences
Original language description
Automatic classification of 3D skeleton sequences of human motions has applications in many domains, ranging from entertainment to medicine. The classification is a difficult problem as the motions belonging to the same class needn't be well segmented and can be performed by subjects of various body sizes in different styles and speeds. The state-of-the-art recognition approaches commonly solve this problem by training recurrent neural networks to learn the contextual dependency in both spatial and temporal domains. In this paper, we employ a distance-based similarity measure, based on deep convolutional features, to search for the k-nearest motions with respect to a query motion being classified. The retrieved neighbors are analyzed and re-ranked by additional measures that are automatically chosen for individual queries. The combination of deep features, dynamism in the similarity-measure selection, and a new kNN classifier brings the highest classification accuracy on a challenging dataset with 130 classes. Moreover, the proposed approach can promptly react to changing training data without any need for a retraining process.
Czech name
—
Czech description
—

Project
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
29th International Conference on Database and Expert Systems Applications (DEXA 2018)
ISBN
9783319988115
ISSN
0302-9743
e-ISSN
—
Number of pages
16
Pages from-to
50-65
Publisher name
Springer
Place of publication
Switzerland
Event location
Regensburg, Germany
Event date
Jan 1, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000460551600004

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