Benchmarking Search and Annotation in Continuous Human Skeleton
Sequences

D 2019

Benchmarking Search and Annotation in Continuous Human Skeleton Sequences

SEDMIDUBSKÝ, Jan, Petr ELIÁŠ a Pavel ZEZULA

Základní údaje

Originální název

Benchmarking Search and Annotation in Continuous Human Skeleton Sequences

Autoři

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

Vydání

New York, NY, USA, International Conference on Multimedia Retrieval (ICMR), od s. 38-42, 5 s. 2019

Nakladatel

ACM

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/19:00107371

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-6765-3

DOI

http://dx.doi.org/10.1145/3323873.3325013

UT WoS

000482188900008

Klíčová slova anglicky

motion capture dataset;continuous 3D skeleton sequence;stream-based processing;benchmark;subsequence search;action detection;mining

Štítky

core_B, DISA, firank_A

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 15. 4. 2020 10:19, doc. RNDr. Jan Sedmidubský, Ph.D.

Anotace

V originále

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.

Návaznosti

GA19-02033S, projekt VaV

Název: Vyhledávání, analytika a anotace datových toků lidských pohybů

Investor: Grantová agentura ČR, Searching, Mining, and Annotating Human Motion Streams

Citovat

SEDMIDUBSKÝ, Jan, Petr ELIÁŠ a Pavel ZEZULA. Benchmarking Search and Annotation in Continuous Human Skeleton Sequences. Online. In International Conference on Multimedia Retrieval (ICMR). New York, NY, USA: ACM, 2019, s. 38-42. ISBN 978-1-4503-6765-3. Dostupné z: https://dx.doi.org/10.1145/3323873.3325013.

@inproceedings{1525496,
   author = {Sedmidubský, Jan and Eliáš, Petr and Zezula, Pavel},
   address = {New York, NY, USA},
   booktitle = {International Conference on Multimedia Retrieval (ICMR)},
   doi = {http://dx.doi.org/10.1145/3323873.3325013},
   keywords = {motion capture dataset;continuous 3D skeleton sequence;stream-based processing;benchmark;subsequence search;action detection;mining},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, USA},
   isbn = {978-1-4503-6765-3},
   pages = {38-42},
   publisher = {ACM},
   title = {Benchmarking Search and Annotation in Continuous Human Skeleton Sequences},
   year = {2019}
}

TY  - JOUR
ID  - 1525496
AU  - Sedmidubský, Jan - Eliáš, Petr - Zezula, Pavel
PY  - 2019
TI  - Benchmarking Search and Annotation in Continuous Human Skeleton Sequences
PB  - ACM
CY  - New York, NY, USA
SN  - 9781450367653
KW  - motion capture dataset;continuous 3D skeleton sequence;stream-based processing;benchmark;subsequence search;action detection;mining
N2  - 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.
ER  -

SEDMIDUBSKÝ, Jan, Petr ELIÁŠ a Pavel ZEZULA. Benchmarking Search and Annotation in Continuous Human Skeleton Sequences. Online. In \textit{International Conference on Multimedia Retrieval (ICMR)}. New York, NY, USA: ACM, 2019, s.~38-42. ISBN~978-1-4503-6765-3. Dostupné z: https://dx.doi.org/10.1145/3323873.3325013.

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