Vehicle Make and Model Recognition (VMMR) systems provide a fully automatic framework to recognize and classify different vehicle models. Several approaches have been proposed to address this challenge; however, they can perform in restricted conditions. Here, in this paper, we formulate the VMMR as a fine-grained classification problem and propose a new configurable on-road VMMR framework. We benefit from the unsupervised feature learning methods, and in more details, we employ Locality-constraint Linear Coding (LLC) method as a fast feature encoder for encoding the input SIFT features. The proposed method can perform in real environments of different conditions. This framework can recognize 50 models of vehicles and has the advantage to classify every other vehicle not belonging to one of the specified 50 classes as an unknown vehicle. The proposed VMMR framework can be configured to become faster or more accurate based on the application domain. The proposed approach is examined on two datasets, including Iranian on-road vehicle (IORV) dataset and CompuCar dataset. The IORV dataset contains images of 50 models of vehicles captured in real situations by traffic-cameras in different weather and lighting conditions. The experimental results show the advantage of the real-time configuration of the proposed framework over the state-of-the-art methods on the IORV datatset and comparable results on CompuCar dataset with 97.5% and 98.4% accuracies, respectively and acceptable running time.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-Time Vehicle Make and Model Recognition Using Unsupervised Feature Learning


    Beteiligte:


    Erscheinungsdatum :

    2020-07-01


    Format / Umfang :

    3720891 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real-time unsupervised feature model generation for a vehicle following system

    Fries, Carsten / Wuensche, Hans-Joachim | IEEE | 2016




    Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features

    Siddiqui, Abdul Jabbar / Mammeri, Abdelhamid / Boukerche, Azzedine | IEEE | 2016


    Vehicle make and model recognition using bag of expressions

    Jamil, Adeel Ahmad / Hussain, Fawad / Yousaf, Muhammad Haroon et al. | BASE | 2020

    Freier Zugriff