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@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 and Giuseppe AMATO. SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval. Online. In \textit{45th European Conference on Information Retrieval (ECIR)}. Cham: Springer, 2023, p.~110-124. ISBN~978-3-031-28237-9. Available from: https://dx.doi.org/10.1007/978-3-031-28238-6\_{}8.
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