| Title |
Intelligent Management System: Deep Convolutional Neural Networks for Automatic Attribute Recognition in IP Surveillance Networks |
| ID_Doc |
44510 |
| Authors |
Kao, CC; Lai, YC; Pei, J; Chang, CW; Kuo, FH; Shun, JY |
| Title |
Intelligent Management System: Deep Convolutional Neural Networks for Automatic Attribute Recognition in IP Surveillance Networks |
| Year |
2020 |
| Published |
|
| DOI |
10.23919/apnoms50412.2020.9237043 |
| Abstract |
In recent years, IP surveillance networks are expected to enable various practical applications, such as finding suspects, monitoring pedestrians, and securing societies (e.g., securing a city, a company and a data center). With these applications, IP surveillance network is regarded as one of the potential technologies toward developing smart cities. To support the concept of IP surveillance networks, automatic attribute recognition systems have emerged as a promising intelligent management system. To automatically recognize attributes of pedestrians (e.g., gender and clothing), we apply deep convolutional neural networks (CNNs), and the main contributions of this paper are threefold: (1) we proposed a practical system architecture for intelligent management of surveillance networks; (2) we implemented different deep CNNs, and an ensemble-learning method that leverages these multiple deep-learning models; (3) we evaluated the models using the real data of IP surveillance networks. |
| Author Keywords |
attribute recognition; deep learning; intelligent management; smart city; surveillance networks |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000630323900082 |
| WoS Category |
Telecommunications |
| Research Area |
Telecommunications |
| PDF |
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