Vehicle logo, as the key information of vehicle, combined with other vehicle characteristics will make vehicle management more effective in the intelligent transportation system. However, it is still a challenging task to extract effective features for vehicle logo recognition, largely due to its variations in illumination and low resolution. Aiming at improving the recognition rate of vehicle logo recognition, this paper proposes a new vehicle logo recognition method. First, in the aspect of vehicle logo feature extraction, a vehicle logo feature extraction algorithm based on the fusion of SIFT features and Dense-SIFT features was put forward to generate local feature descriptors. Then Bag-of-words model was used to describe vehicle logo features and form visual dictionary histogram. Considering that bag-of-words model ignores spatial structure information of objects, we introduced spatial pyramid model into bag-of-words model. In the aspect of vehicle logo recognition, vehicle logo was classified by using Support Vector Machine (SVM) based on one-against-the-rest multiclassification structure. Finally, our method was verified effectively through the experiment compared to other methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A vehicle logo recognition method based on improved SIFT feature and bag-of-words model


    Contributors:
    Huang, Zhiliang (author) / Lu, Xiaobo (author) / Chen, Cong (author)

    Conference:

    Tenth International Conference on Digital Image Processing (ICDIP 2018) ; 2018 ; Shanghai,China


    Published in:

    Proc. SPIE ; 10806


    Publication date :

    2018-08-09





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    M-SIFT: A new method for Vehicle Logo Recognition

    Psyllos, Apostolos / Anagnostopoulos, Christos-Nikolaos / Kayafas, Eleftherios | IEEE | 2012


    Vehicle Logo Recognition Using a SIFT-Based Enhanced Matching Scheme

    Psyllos, Apostolos P / Anagnostopoulos, Christos-Nikolaos E / Kayafas, Eleftherios | IEEE | 2010



    Vehicle Logo Recognition

    Li, Yang | DataCite | 2024


    Vehicle Recognition Using Improved SIFT and Multi-View Model

    Hua, L. / Xu, W. / Wang, T. et al. | British Library Online Contents | 2013