The vision-based surveillance systems are widely used in analysis traffic information due to their ease of installation and accuracy of their results. In this paper, an image processing system in real-time has been proposed to detect and classify the vehicles at intersections. This information can be used to estimate traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The detection operation is performed by the background subtraction technique, the approximated median filter is used to extract and update the background, then the vehicles will be tracked in the detection area. After that the vehicle classification will be implemented by using the convolutional neural network (CNN). The system is applied to videos obtained by stationary cameras. The experiments demonstrate that this system is able to robustly detect and classify the vehicles.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Traffic Surveillance System in Real-Time to Detect and Classify Vehicles by Using Convolutional Neural Network


    Beteiligte:


    Erscheinungsdatum :

    01.12.2019


    Format / Umfang :

    1042894 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Using Cross Entropy to Detect and Classify Network Anomalous Traffic

    Yan, R. / Zheng, Q. | British Library Online Contents | 2010


    Voice-Assisted Real-Time Traffic Sign Recognition System Using Convolutional Neural Network

    Manawadu, Mayura / Wijenayake, Udaya | ArXiv | 2024

    Freier Zugriff

    A New Approach to Classify Drones Using a Deep Convolutional Neural Network

    Hrishi Rakshit / Pooneh Bagheri Zadeh | DOAJ | 2024

    Freier Zugriff

    Real-Time Traffic Sign Recognition Using Convolutional Neural Networks

    Rao, Aditya / Motwani, Rahul / Sarguroh, Naveed et al. | Springer Verlag | 2021


    Real-time vehicles detection and traffic parameter extraction for highway surveillance

    Al-Garni, Saad Mohammad / Abdennour, Adel | Tema Archiv | 2009