The level of precision of deep neural networks in visual perception tasks allows to capture crucial information from the environment for future projects, such as autonomous vehicles and smart cities. One possibility that this type of system would allow is the control and tracking of certain suspicious vehicles. Considering the use of this technology by police, it would facilitate the tracking of certain cars under investigation. With this vision, the objective of this work is the study of the current state-of-the-art of the methods and the development of a system that solves two tasks efficiently: the visual characterization and re-identification of vehicles and the license plates segmentation and character recognition. This dual identification can adapt to the environmental conditions, target distance and cameras capabilities and resolution. To test and validate this system, a custom dataset has been created to minimize the difference between lab and real environment.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning for Robust Vehicle Identification


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Tardioli, Danilo (Herausgeber:in) / Matellán, Vicente (Herausgeber:in) / Heredia, Guillermo (Herausgeber:in) / Silva, Manuel F. (Herausgeber:in) / Marques, Lino (Herausgeber:in) / Ramajo Ballester, Álvaro (Autor:in) / González Cepeda, Jacobo (Autor:in) / Armingol Moreno, José María (Autor:in)

    Kongress:

    Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022



    Erscheinungsdatum :

    19.11.2022


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Unmanned aerial vehicle identification system based on deep learning

    HUI LIN | Europäisches Patentamt | 2024

    Freier Zugriff

    Vehicle Re-Identification Using Quadruple Directional Deep Learning Features

    Zhu, Jianqing / Zeng, Huanqiang / Huang, Jingchang et al. | IEEE | 2020


    Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems

    Ferdowsi, Aidin / Challita, Ursula / Saad, Walid et al. | IEEE | 2018


    Vehicle Identification Using Automotive LIDAR VLP-16 and Deep Learning

    Keertheeswaran, M. P. / Nair, Binoy B. | TIBKAT | 2021