In this paper, a CNN-based vehicle detection and retrieval framework is proposed for the intelligent transportation system. Firstly, the vehicle target is detected from the traffic scene. The proposed object detection method uses a fully convolutional neural network (CNN) based on SqueezeNet, which has the characteristics of real-time, high accuracy and has small model size. Secondly, an intra-class image retrieval method is presented to search vehicles which are similar to the target vehicle in the dataset. The image retrieval results can be used for traffic scenes simulation and modeling. The experiments and comparisons prove the effectiveness of our framework.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Jointly Detecting and Retrieving Vehicles from Road Image Sequences based on CNN


    Beteiligte:
    Wu, Xiao (Autor:in) / Li, Yaochen (Autor:in) / Liu, Yuehu (Autor:in) / Pang, Shanmin (Autor:in) / Wang, Le (Autor:in) / Wu, Chuan (Autor:in) / Huo, Huihui (Autor:in)


    Erscheinungsdatum :

    2019-06-01


    Format / Umfang :

    3406629 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    JOINTLY DETECTING AND RETRIEVING VEHICLES FROM ROAD IMAGE SEQUENCES BASED ON CNN

    Wu, Xiao / Li, Yaochen / Liu, Yuehu et al. | British Library Conference Proceedings | 2019


    Retrieving Road Surface Profiles from PSDs for Ride Simulation of Vehicles

    Munari, L.A. / Fontanella, L. / Hoss, L. et al. | British Library Conference Proceedings | 2012


    Retrieving Road Surface Profiles from PSDs for Ride Simulation of Vehicles

    Hoss, Leonardo / Fontanella, Luan / Munari, Luiz A. et al. | SAE Technical Papers | 2012


    Memory-based Forecasting of Complex Natural Patterns by Retrieving Similar Image Sequences

    Otsuka, K. / Horikoshi, T. / Suzuki, S. et al. | British Library Conference Proceedings | 1999


    Detecting Road Conditions Based on Braking Event Data Received from Vehicles

    GOLOV GIL | Europäisches Patentamt | 2019

    Freier Zugriff