D 2018

Similarity-Based Processing of Motion Capture Data

SEDMIDUBSKÝ, Jan a Pavel ZEZULA

Základní údaje

Originální název

Similarity-Based Processing of Motion Capture Data

Autoři

SEDMIDUBSKÝ, Jan (203 Česká republika, garant, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

New York, NY, USA, Proceedings of the ACM Conference on Multimedia (MM 2018), od s. 2087-2089, 3 s. 2018

Nakladatel

ACM

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/18:00103375

Organizace

Fakulta informatiky – Masarykova univerzita – Repozitář

ISBN

978-1-4503-5665-7

UT WoS

000509665700261

EID Scopus

2-s2.0-85058215908

Klíčová slova anglicky

action detection; annotation; motion capture data; similarity searching; stream-based processing; subsequence matching

Návaznosti

EF16_019/0000822, projekt VaV.
Změněno: 6. 9. 2020 02:37, RNDr. Daniel Jakubík

Anotace

V originále

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.
Zobrazeno: 6. 7. 2025 15:45