Name
Title | ||
|---|---|---|
A Real-Time and Hardware-Efficient Processor for Skeleton-Based Action Recognition With Lightweight Convolutional Neural Network. |
Abstract | ||
|---|---|---|
Skeleton-based human action recognition (HAR) has been extensively studied these years because body skeleton has the simple but informative representation of human action, which greatly reduces the computation complexity compared with the image-based HAR. As a result, it is suitable for low power implementation in embedded platforms. In this brief, we present a systematic approach to developing a ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCSII.2019.2899829 | IEEE Transactions on Circuits and Systems II: Express Briefs |
Keywords | Field | DocType |
Skeleton,Convolution,Hardware,Feature extraction,Buffer storage,Image sensors,Two dimensional displays | Convolutional neural network,Action recognition,Computer hardware,Mathematics | Journal |
Volume | Issue | ISSN |
66 | 12 | 1549-7747 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingyi Zhang | 1 | 10 | 5.44 |
| Jun Han | 2 | 14 | 7.15 |
| Zhize Huang | 3 | 2 | 0.38 |
| Jianwei Yang | 4 | 58 | 12.73 |
| Xiaoyang Zeng | 5 | 442 | 107.26 |