| Title |
A Low-Cost Embedded Car Counter System by using Jetson Nano Based on Computer Vision and Internet of Things |
| ID_Doc |
41868 |
| Authors |
Othman, NA; Saleh, ZZ; Ibrahim, BR |
| Title |
A Low-Cost Embedded Car Counter System by using Jetson Nano Based on Computer Vision and Internet of Things |
| Year |
2022 |
| Published |
|
| DOI |
10.1109/DASA54658.2022.9765087 |
| Abstract |
With the increasing volume of cars in traffic and the global traffic increasing exponentially, it has become critical to manage traffic as a challenge in the most developed countries. To address this issue, the intelligent traffic control system will use automatic vehicle counting as one of its core tasks to facilitate access, particularly in parking lots. The primary benefit of automatic vehicle counting is that it allows for managing and evaluating traffic conditions in the urban transportation system. The new era of technologies such as the Internet of Things and computer vision has transformed traditional systems into new smart city networks. Because of the proliferation of computer vision, traffic counting from low-cost control cameras may emerge as an appealing candidate for traffic flow control automation. This paper proposed a low-cost embedded car counter system using a Jetson nano card based on computer vision and IoT technologies to implement the offered system. In the proposed system, we apply a combination of background subtraction and counters, trackable objects, centroid tracking, and direction counting. Moreover, we implement the MoG foreground-background subtractor method. The proposed system is connected to the Internet using Telegram API to send notifications to smartphone hourly to analyze traffic congestion. In addition, we compared the performance of Jetson nano with the Raspberry Pi4 platform. |
| Author Keywords |
Car counter; Internet of Things; Smart city; Computer vision; Jetson nano |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000839386600271 |
| WoS Category |
Computer Science, Artificial Intelligence; Operations Research & Management Science |
| Research Area |
Computer Science; Operations Research & Management Science |
| PDF |
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