| Title |
Online Detection of Action Start via Soft Computing for Smart City |
| ID_Doc |
38796 |
| Authors |
Wang, T; Chen, Y; Lv, HQ; Teng, J; Snoussi, H; Tao, F |
| Title |
Online Detection of Action Start via Soft Computing for Smart City |
| Year |
2021 |
| Published |
Ieee Transactions On Industrial Informatics, 17, 1 |
| DOI |
10.1109/TII.2020.2997032 |
| Abstract |
Soft computing is facing a rapid evolution thanks to the development of artificial intelligence especially the deep learning. With video surveillance technologies of soft computing, such as image processing, computer vision, and pattern recognition combined with cloud computing, the construction of smart cities could be maintained and greatly enhanced. In this article, we focus on the online detection of action start task in video understanding and analysis, which is critical to the multimedia security in smart cities. We propose a novel model to tackle this problem and achieves state-of-the-art results on the benchmark THUMOS14 data set. |
| Author Keywords |
Task analysis; Streaming media; Smart cities; Real-time systems; Machine learning; Semantics; Cloud computing; Action start; cloud computing; online detection; smart city; soft computing; video analysis |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000587719200049 |
| WoS Category |
Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial |
| Research Area |
Automation & Control Systems; Computer Science; Engineering |
| PDF |
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