Recognizing User-Defined Subsequences in Human Motion Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107370" target="_blank" >RIV/00216224:14330/19:00107370 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1145/3323873.3326922" target="_blank" >http://dx.doi.org/10.1145/3323873.3326922</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3323873.3326922" target="_blank" >10.1145/3323873.3326922</a>
Alternative languages
Result language
angličtina
Original language name
Recognizing User-Defined Subsequences in Human Motion Data
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/GA19-02033S" target="_blank" >GA19-02033S: Searching, Mining, and Annotating Human Motion Streams</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference on Multimedia Retrieval (ICMR)
ISBN
9781450367653
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
395-398
Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Ottawa, Canada
Event date
Jan 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000482188900058