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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Low-Cost Embedded Car Counter System by using Jetson Nano Based on Computer Vision and Internet of Things




    Publication date :

    2022-03-23


    Size :

    1032209 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Comparative Study of Computer Vision Based Line Followers Using Raspberry Pi and Jetson Nano

    Dewantoro, Gunawan / Mansuri, Jamil / Setiaji, Fransiscus Dalu | BASE | 2021

    Free access

    Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB

    Cahyo, Muhammad Pandu Dwi / Utaminingrum, Fitri | BASE | 2022

    Free access

    Multi-band sub-GHz technology recognition on NVIDIA’s Jetson Nano

    Fontaine, Jaron / Shahid, Adnan / Elsas, Robbe et al. | IEEE | 2020


    Facial Mask Detection and Energy Monitoring Dashboard Using YOLOv5 and Jetson Nano

    binti Amir Hamzah, Nur Asyiqin / bin Abd Ghani, Hadhrami / Al-Selwi, Hatem Fahd et al. | Springer Verlag | 2022

    Free access

    Real Time Object Detection of Aerial Images Using Deep Learning on Jetson Nano

    Wadhwa, Shruti / Saini, Poonam / Kumar, Rakesh et al. | AIAA | 2025