In this paper, we propose a vehicle type classification method using a semisupervised convolutional neural network from vehicle frontal-view images. In order to capture rich and discriminative information of vehicles, we introduce sparse Laplacian filter learning to obtain the filters of the network with large amounts of unlabeled data. Serving as the output layer of the network, the softmax classifier is trained by multitask learning with small amounts of labeled data. For a given vehicle image, the network can provide the probability of each type to which the vehicle belongs. Unlike traditional methods by using handcrafted visual features, our method is able to automatically learn good features for the classification task. The learned features are discriminative enough to work well in complex scenes. We build the challenging BIT-Vehicle dataset, including 9850 high-resolution vehicle frontal-view images. Experimental results on our own dataset and a public dataset demonstrate the effectiveness of the proposed method.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vehicle Type Classification Using a Semisupervised Convolutional Neural Network




    Erscheinungsdatum :

    2015




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik



    Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

    Dong, Zhen / Wu, Yuwei / Pei, Mingtao et al. | IEEE | 2015


    Locally connected graph embedding for semisupervised image classification

    Lu, K. / Ding, Z. / Zhao, J. | British Library Online Contents | 2012


    Classification of Brain Tumor Using Convolutional Neural Network

    Pathak, Miss Krishna / Pavthawala, Mahekkumar / Patel, Miss Nirali et al. | IEEE | 2019



    Emergency Vehicle Detection Using Deep Convolutional Neural Network

    Haque, Samiul / Sharmin, Shayla / Deb, Kaushik | Springer Verlag | 2022