Similarity-Based Processing of Motion Capture Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00103375" target="_blank" >RIV/00216224:14330/18:00103375 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1145/3240508.3241468" target="_blank" >http://dx.doi.org/10.1145/3240508.3241468</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3240508.3241468" target="_blank" >10.1145/3240508.3241468</a>

Result language

angličtina

Original language name

Similarity-Based Processing of Motion Capture Data

Original language description

Motion capture technologies digitize human movements by tracking 3D positions of specific skeleton joints in time. Such spatio-temporal 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. The recorded data can be imprecise, voluminous, and the same movement action can be performed by various subjects in a number of alternatives that can vary in speed, timing or a position in space. This requires employing completely different data-processing paradigms compared to the traditional domains such as attributes, text or images. The objective of this tutorial is to explain fundamental principles and technologies designed for similarity comparison, searching, subsequence matching, classification and action detection in the motion capture data. Specifically, we emphasize the importance of similarity needed to express the degree of accordance between pairs of motion sequences and also discuss the machine-learning approaches able to automatically acquire content-descriptive movement features. We explain how the concept of similarity together with the learned features can be employed for searching similar occurrences of interested actions within a long motion sequence. Assuming a user-provided categorization of example motions, we discuss techniques able to recognize types of specific movement actions and detect such kinds of actions within continuous motion sequences. Selected operations will be demonstrated by on-line web applications.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10200 - Computer and information sciences

Project

<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</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

Proceedings of the ACM Conference on Multimedia (MM 2018)

ISBN

9781450356657

ISSN

—

e-ISSN

—

Number of pages

3

Pages from-to

2087-2089

Publisher name

ACM

Place of publication

New York, NY, USA

Event location

Seoul, Republic of Korea

Event date

Jan 1, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000509665700261