| Title |
A Deep Learning Tool to Classify Vehicles in Real Time |
| ID_Doc |
43009 |
| Authors |
Farias, H; Solar, M; Ortiz, D |
| Title |
A Deep Learning Tool to Classify Vehicles in Real Time |
| Year |
2021 |
| Published |
|
| DOI |
10.1109/ICEDEG52154.2021.9530932 |
| Abstract |
The problem of vehicular congestion is transversal to all the cities worldwide. To deal with it, it can be done from the construction of new roads or the management of these as in the case of exclusive roads for public transport. In the latter case, information is necessary. Currently, in Chile this collection process is manually done, so it does not respond to current information needs, because the long time the process takes. The use of deep learning models for automatic counting has shown outstanding results, but based on a scheme for sending video streaming to remote servers. The present proposal aims to not depend on fiber optic connectivity. It is based on a network of modules based on Edge Artificial Intelligence (Edge AI) in order to build a scalable, real-time data capture platform. |
| Author Keywords |
deep learning; smart city; smart mobility |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) |
| EID |
WOS:000847020200030 |
| WoS Category |
Computer Science, Information Systems; Communication; Computer Science, Interdisciplinary Applications; Political Science; Public Administration |
| Research Area |
Computer Science; Communication; Government & Law; Public Administration |
| PDF |
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