| Title |
Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City |
| ID_Doc |
38227 |
| Authors |
Barthélemy, J; Verstaevel, N; Forehead, H; Perez, P |
| Title |
Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City |
| Year |
2019 |
| Published |
Sensors, 19, 9 |
| DOI |
10.3390/s19092048 |
| Abstract |
The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project's aim was to design and evaluate an edge-computing device using computer vision and deep neural networks to track in real-time multi-modal transportation while ensuring citizens' privacy. The performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable Agnosticity framework designed to collect, store and access data from multiple sensors, with results from two real-world experiments. |
| Author Keywords |
edge-computing; IoT; smart city; video analytic; traffic monitoring; CCTV |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000469766800092 |
| WoS Category |
Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation |
| Research Area |
Chemistry; Engineering; Instruments & Instrumentation |
| PDF |
https://www.mdpi.com/1424-8220/19/9/2048/pdf?version=1556781090
|