Další formáty:
BibTeX
LaTeX
RIS
@inproceedings{2245929,
author = {Sedmidubský, Jan and Carrara, Fabio and Amato, Giuseppe},
address = {Cham},
booktitle = {45th European Conference on Information Retrieval (ECIR)},
doi = {http://dx.doi.org/10.1007/978-3-031-28238-6_8},
keywords = {3D skeleton sequence;segment similarity;unsupervised feature learning;Variational AutoEncoder;segment code list;action retrieval},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Cham},
isbn = {978-3-031-28237-9},
pages = {110-124},
publisher = {Springer},
title = {SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval},
year = {2023}
}
TY - JOUR
ID - 2245929
AU - Sedmidubský, Jan - Carrara, Fabio - Amato, Giuseppe
PY - 2023
TI - SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval
PB - Springer
CY - Cham
SN - 9783031282379
KW - 3D skeleton sequence;segment similarity;unsupervised feature learning;Variational AutoEncoder;segment code list;action retrieval
N2 - Recent progress in pose-estimation methods enables the extraction of sufficiently-precise 3D human skeleton data from ordinary videos, which offers great opportunities for a wide range of applications. However, such spatio-temporal data are typically extracted in the form of a continuous skeleton sequence without any information about semantic segmentation or annotation. To make the extracted data reusable for further processing, there is a need to access them based on their content. In this paper, we introduce a universal retrieval approach that compares any two skeleton sequences based on temporal order and similarities of their underlying segments. The similarity of segments is determined by their content-preserving low-dimensional code representation that is learned using the Variational AutoEncoder principle in an unsupervised way. The quality of the proposed representation is validated in retrieval and classification scenarios; our proposal outperforms the state-of-the-art approaches in effectiveness and reaches speed-ups up to 64x on common skeleton sequence datasets.
ER -
SEDMIDUBSKÝ, Jan, Fabio CARRARA a Giuseppe AMATO. SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval. Online. In \textit{45th European Conference on Information Retrieval (ECIR)}. Cham: Springer, 2023, s.~110-124. ISBN~978-3-031-28237-9. Dostupné z: https://dx.doi.org/10.1007/978-3-031-28238-6\_{}8.
|
