Effective and Efficient Similarity Searching in Motion Capture Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00100703" target="_blank" >RIV/00216224:14330/18:00100703 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11042-017-4859-7" target="_blank" >http://dx.doi.org/10.1007/s11042-017-4859-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-017-4859-7" target="_blank" >10.1007/s11042-017-4859-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Effective and Efficient Similarity Searching in Motion Capture Data

Popis výsledku v původním jazyce

Motion capture data describe human movements in the form of spatio-temporal trajectories of skeleton joints. Intelligent management of such complex data is a challenging task for computers which requires an effective concept of motion similarity. However, evaluating the pair-wise similarity is a difficult problem as a single action can be performed by various actors in different ways, speeds or starting positions. Recent methods usually model the motion similarity by comparing customized features using distance-based functions or specialized machine-learning classifiers. By combining both these approaches, we transform the problem of comparing motions of variable sizes into the problem of comparing fixed-size vectors. Specifically, each rather-short motion is encoded into a compact visual representation from which a highly descriptive 4,096-dimensional feature vector is extracted using a fine-tuned deep convolutional neural network. The advantage is that the fixed-size features are compared by the Euclidean distance which enables efficient motion indexing by any metric-based index structure. Another advantage of the proposed approach is its tolerance towards an imprecise action segmentation, the variance in movement speed, and a lower data quality. All these properties together bring new possibilities for effective and efficient large-scale retrieval.

Název v anglickém jazyce

Effective and Efficient Similarity Searching in Motion Capture Data

Popis výsledku anglicky

Motion capture data describe human movements in the form of spatio-temporal trajectories of skeleton joints. Intelligent management of such complex data is a challenging task for computers which requires an effective concept of motion similarity. However, evaluating the pair-wise similarity is a difficult problem as a single action can be performed by various actors in different ways, speeds or starting positions. Recent methods usually model the motion similarity by comparing customized features using distance-based functions or specialized machine-learning classifiers. By combining both these approaches, we transform the problem of comparing motions of variable sizes into the problem of comparing fixed-size vectors. Specifically, each rather-short motion is encoded into a compact visual representation from which a highly descriptive 4,096-dimensional feature vector is extracted using a fine-tuned deep convolutional neural network. The advantage is that the fixed-size features are compared by the Euclidean distance which enables efficient motion indexing by any metric-based index structure. Another advantage of the proposed approach is its tolerance towards an imprecise action segmentation, the variance in movement speed, and a lower data quality. All these properties together bring new possibilities for effective and efficient large-scale retrieval.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

1573-7721

Svazek periodika

77

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

12073-12094

Kód UT WoS článku

000433202100021

EID výsledku v databázi Scopus

2-s2.0-85019711344