| Title |
Human Behavior Deep Recognition Architecture for Smart City Applications in the 5G Environment |
| ID_Doc |
37918 |
| Authors |
Dai, C; Liu, XG; Lai, JF; Li, P; Chao, HC |
| Title |
Human Behavior Deep Recognition Architecture for Smart City Applications in the 5G Environment |
| Year |
2019 |
| Published |
Ieee Network, 33, 5 |
| DOI |
10.1109/MNET.2019.1800310 |
| Abstract |
Human behavior recognition (HBR), as a critical link for further intelligent and real-time smart city application design, has attracted much more attention in recent years. Although the related technologies have been developed rapidly and many solid achievements have been already obtained, there is still a lot of space to deeply enhance the related research including the recognition structures, algorithms, and so on, to meet the increasing requirements of Smart City construction. In this article, we first review the conventional HBR structure, and analyze the problems and challenges for future smart city applications. Then a parallel and multi-layer deep recognition architecture (PMDRA) is discussed, which could have more powerful and ubiquitous feature extraction ability because of the hierarchical utilization of the deep learning network. Meanwhile, the quantity adjustment mechanism for DRUs and DLNUs could help for designing the actual architecture according to the requirements of real scenarios. |
| Author Keywords |
Feature extraction; Deep learning; Smart cities; Task analysis; Streaming media; Internet of Things; Image reconstruction |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000495016000028 |
| WoS Category |
Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
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