| Title |
IoT-Driven Automated Object Detection Algorithm for Urban Surveillance Systems in Smart Cities |
| ID_Doc |
42333 |
| Authors |
Hu, L; Ni, Q |
| Title |
IoT-Driven Automated Object Detection Algorithm for Urban Surveillance Systems in Smart Cities |
| Year |
2018 |
| Published |
Ieee Internet Of Things Journal, 5, 2 |
| DOI |
10.1109/JIOT.2017.2705560 |
| Abstract |
Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoT-driven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems. |
| Author Keywords |
Internet of Things (IoT); smart city; smart transportation; urban surveillance system; vehicle; vehicle license plate |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000429971100028 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
https://eprints.lancs.ac.uk/id/eprint/87352/1/07931539_1_.pdf
|