Name
Abstract | ||
|---|---|---|
Recently, regarding several beneficial properties of depth camera, numerous 3D action recognition frameworks have studied high-level features by exploiting deep learning techniques, but nevertheless they cannot seize the meaningful characteristics of static human pose and dynamic action motion of a whole sequence. This paper introduces a deep network configured by two parallel streams of convolutional stacks for fully learning the deep intra-frame joint associations and inter-frame joint correlations, wherein the structure of each stream is learned from Inception-v3. In experiments, besides the compatibility verification with various backbone networks, the proposed approach achieves the state-of-theart performance in battle with several deep learning-based methods on the updated NTU RGB+D 120 dataset. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICASSP40776.2020.9054392 | ICASSP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thien Huynh-The | 1 | 94 | 21.54 |
| Cam-Hao Hua | 2 | 45 | 11.22 |
| Tu Anh T. Nguyen | 3 | 56 | 9.27 |
| Dong Seong Kim | 4 | 866 | 93.34 |