Recognizing User-Defined Subsequences in Human Motion Data

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Recognizing User-Defined Subsequences in Human Motion Data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Recognizing User-Defined Subsequences in Human Motion Data

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/GA19-02033S" target="_blank" >GA19-02033S: Vyhledávání, analytika a anotace datových toků lidských pohybů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Conference on Multimedia Retrieval (ICMR)

ISBN

9781450367653

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

395-398

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Ottawa, Canada

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000482188900058