Další formáty:
BibTeX
LaTeX
RIS
@article{1773602, author = {Sedmidubský, Jan and Eliáš, Petr and Budíková, Petra and Zezula, Pavel}, article_number = {26 April 2021}, doi = {http://dx.doi.org/10.1109/ACCESS.2021.3075766}, keywords = {Action detection; content-based processing; deep features; metric learning; motion capture data; skeleton sequences; similarity; sub-sequence search}, language = {eng}, issn = {2169-3536}, journal = {IEEE Access}, title = {Content-Based Management of Human Motion Data: Survey and Challenges}, url = {https://ieeexplore.ieee.org/document/9416451}, volume = {9}, year = {2021} }
TY - JOUR ID - 1773602 AU - Sedmidubský, Jan - Eliáš, Petr - Budíková, Petra - Zezula, Pavel PY - 2021 TI - Content-Based Management of Human Motion Data: Survey and Challenges JF - IEEE Access VL - 9 IS - 26 April 2021 SP - 64241-64255 EP - 64241-64255 PB - IEEE Xplore Digital Library SN - 21693536 KW - Action detection KW - content-based processing KW - deep features KW - metric learning KW - motion capture data KW - skeleton sequences KW - similarity KW - sub-sequence search UR - https://ieeexplore.ieee.org/document/9416451 N2 - Digitization of human motion using skeleton representations offers exciting possibilities for a large number of applications but, at the same time, requires innovative techniques for their effective and efficient processing. Content-based processing of skeleton data has developed rapidly in recent years, focusing mainly on specialized prototypes with limited consideration of generic data management possibilities. In this survey article, we synthesize and categorize the existing approaches and outline future research challenges brought by the increasing availability of human motion data. In particular, we first discuss the problems of suitable representation and segmentation of continuous skeleton data obtained from various sources. Then, we concentrate on comparison models for assessing the similarity of time-restricted pieces of motions, as required by any content-based management operation. Next, we review the techniques for evaluating similarity queries over collections of motion sequences and filtering query-relevant parts from continuous motion streams. Finally, we summarize the usability of existing techniques in perspective application domains and discuss the new challenges related to current technological and infrastructural developments. We especially assess the existing techniques from the perspective of scalability and propose future research directions for dealing with large and diverse volumes of skeleton data. ER -
SEDMIDUBSKÝ, Jan, Petr ELIÁŠ, Petra BUDÍKOVÁ a Pavel ZEZULA. Content-Based Management of Human Motion Data: Survey and Challenges. \textit{IEEE Access}. IEEE Xplore Digital Library, 2021, roč.~9, 26 April 2021, s.~64241-64255. ISSN~2169-3536. Dostupné z: https://dx.doi.org/10.1109/ACCESS.2021.3075766.
|