Benchmarking Search and Annotation in Continuous Human Skeleton Sequences

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107371" target="_blank" >RIV/00216224:14330/19:00107371 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3323873.3325013" target="_blank" >http://dx.doi.org/10.1145/3323873.3325013</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3323873.3325013" target="_blank" >10.1145/3323873.3325013</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Benchmarking Search and Annotation in Continuous Human Skeleton Sequences

Popis výsledku v původním jazyce

Motion capture data are digital representations of human movements in form of 3D trajectories of multiple body joints. To understand the captured motions, similarity-based processing and deep learning have already proved to be effective, especially in classifying pre-segmented actions. However, in real-world scenarios motion data are typically captured as long continuous sequences, without explicit knowledge of semantic partitioning. To make such unsegmented data accessible and reusable as required by many applications, there is a strong requirement to analyze, search, annotate and mine them automatically. However, there is currently an absence of datasets and benchmarks to test and compare the capabilities of the developed techniques for continuous motion data processing. In this paper, we introduce a new large-scale LSMB19 dataset consisting of two 3D skeleton sequences of a total length of 54.5 hours. We also define a benchmark on two important multimedia retrieval operations: subsequence search and annotation. Additionally, we exemplify the usability of the benchmark by establishing baseline results for these operations.

Název v anglickém jazyce

Benchmarking Search and Annotation in Continuous Human Skeleton Sequences

Popis výsledku anglicky

Motion capture data are digital representations of human movements in form of 3D trajectories of multiple body joints. To understand the captured motions, similarity-based processing and deep learning have already proved to be effective, especially in classifying pre-segmented actions. However, in real-world scenarios motion data are typically captured as long continuous sequences, without explicit knowledge of semantic partitioning. To make such unsegmented data accessible and reusable as required by many applications, there is a strong requirement to analyze, search, annotate and mine them automatically. However, there is currently an absence of datasets and benchmarks to test and compare the capabilities of the developed techniques for continuous motion data processing. In this paper, we introduce a new large-scale LSMB19 dataset consisting of two 3D skeleton sequences of a total length of 54.5 hours. We also define a benchmark on two important multimedia retrieval operations: subsequence search and annotation. Additionally, we exemplify the usability of the benchmark by establishing baseline results for these operations.

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

5

Strana od-do

38-42

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

000482188900008