|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM355201364 |
| 003 |
DE-627 |
| 005 |
20231226063838.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2022 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2022.3230249
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1183.xml
|
| 035 |
|
|
|a (DE-627)NLM355201364
|
| 035 |
|
|
|a (NLM)37015390
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Zhu, Yisheng
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Multilevel Spatial-Temporal Excited Graph Network for Skeleton-based Action Recognition
|
| 264 |
|
1 |
|c 2022
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 04.04.2023
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status Publisher
|
| 520 |
|
|
|a The ability to capture joint connections in complicated motion is essential for skeleton-based action recognition. However, earlier approaches may not be able to fully explore this connection in either the spatial or temporal dimension due to fixed or single-level topological structures and insufficient temporal modeling. In this paper, we propose a novel multilevel spatial-temporal excited graph network (ML-STGNet) to address the above problems. In the spatial configuration, we decouple the learning of the human skeleton into general and individual graphs by designing a multilevel graph convolution (ML-GCN) network and a spatial data-driven excitation (SDE) module, respectively. ML-GCN leverages joint-level, part-level, and body-level graphs to comprehensively model the hierarchical relations of a human body. Based on this, SDE is further introduced to handle the diverse joint relations of different samples in a data-dependent way. This decoupling approach not only increases the flexibility of the model for graph construction but also enables the generality to adapt to various data samples. In the temporal configuration, we apply the concept of temporal difference to the human skeleton and design an efficient temporal motion excitation (TME) module to highlight the motion-sensitive features. Furthermore, a simplified multiscale temporal convolution (MS-TCN) network is introduced to enrich the expression ability of temporal features. Extensive experiments on the four popular datasets NTU-RGB+D, NTU-RGB+D 120, Kinetics Skeleton 400, and Toyota Smarthome demonstrate that ML-STGNet gains considerable improvements over the existing state of the art
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Shuai, Hui
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Guangcan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Qingshan
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2022) vom: 22. Dez.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:PP
|g year:2022
|g day:22
|g month:12
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2022.3230249
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d PP
|j 2022
|b 22
|c 12
|