Enhancing Effectiveness of Descriptors for Searching and Recognition in Motion Capture Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00094977" target="_blank" >RIV/00216224:14330/17:00094977 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ISM.2017.39" target="_blank" >http://dx.doi.org/10.1109/ISM.2017.39</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISM.2017.39" target="_blank" >10.1109/ISM.2017.39</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Effectiveness of Descriptors for Searching and Recognition in Motion Capture Data

Popis výsledku v původním jazyce

Computer-aided analyses of motion capture data require an effective and efficient concept of motion similarity. Traditional methods generally compare motion sequences by applying time-warping techniques to high-dimensional trajectories of joints. An increasing effectiveness of machine-learning techniques, such as deep convolutional neural networks, brings new possibilities for similarity comparison. Inspired by recent advances in neural networks and image processing, we propose new variants of transformation of motion sequences into 2D images. The generated images are used to fine-tune a neural network from which 4,096D features are extracted and compared by a modified Euclidean distance. The proposed concept is not only efficient but also very effective and outperforms existing methods on a challenging dataset with 130 categories.

Název v anglickém jazyce

Enhancing Effectiveness of Descriptors for Searching and Recognition in Motion Capture Data

Popis výsledku anglicky

Computer-aided analyses of motion capture data require an effective and efficient concept of motion similarity. Traditional methods generally compare motion sequences by applying time-warping techniques to high-dimensional trajectories of joints. An increasing effectiveness of machine-learning techniques, such as deep convolutional neural networks, brings new possibilities for similarity comparison. Inspired by recent advances in neural networks and image processing, we propose new variants of transformation of motion sequences into 2D images. The generated images are used to fine-tune a neural network from which 4,096D features are extracted and compared by a modified Euclidean distance. The proposed concept is not only efficient but also very effective and outperforms existing methods on a challenging dataset with 130 categories.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

19th IEEE International Symposium on Multimedia

ISBN

9781538629376

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

240-243

Název nakladatele

IEEE Computer Society

Místo vydání

Neuveden

Místo konání akce

Taichung, Taiwan

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—