Benchmarking Search and Annotation in Continuous Human Skeleton Sequences

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Benchmarking Search and Annotation in Continuous Human Skeleton Sequences
Original language description
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.
Czech name
—
Czech description
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Project
<a href="/en/project/GA19-02033S" target="_blank" >GA19-02033S: Searching, Mining, and Annotating Human Motion Streams</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
International Conference on Multimedia Retrieval (ICMR)
ISBN
9781450367653
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
38-42
Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Ottawa, Canada
Event date
Jan 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000482188900008

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