We address the problem of traffic sign recognition in a light detection and ranging (LIDAR)-equipped vehicle. With the help of 3-D LIDAR points, the 2-D multiview sign images will be easily detected from the captured images of street signs. After detection, the sign recognition problem is formulated as a multiview object recognition task. We develop a metric-learning-based template matching approach for this task and learn a distance metric between the captured images and the corresponding sign templates. For each sign, recognition is done via soft voting by the recognition results of its corresponding multiview images. We propose a latent structural support vector machine (SVM)-based weakly supervised metric learning (WSMLR) method to learn the metric and a reliability classifier. The reliability classifier is used to determine each image's reliability, which serves as each image's weight in both the learning and soft voting procedure. We evaluate the proposed method for multiview traffic sign recognition on a multiview traffic sign data set with 112 categories and observe very encouraging results compared with other state-of-the-art methods. In addition, the method can be customized to solve the single-view sign recognition. The performance of our method for single-view sign recognition is tested on two public data sets, showing that our method is comparable with other competitive ones.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Weakly Supervised Metric Learning for Traffic Sign Recognition in a LIDAR-Equipped Vehicle


    Contributors:
    Tan, Min (author) / Wang, Baoyuan (author) / Wu, Zhaohui (author) / Wang, Jingdong (author) / Pan, Gang (author)


    Publication date :

    2016-05-01


    Size :

    2370541 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Weakly-Supervised Cross-Domain Dictionary Learning for Visual Recognition

    Zhu, F. / Shao, L. | British Library Online Contents | 2014


    Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition

    Fergus, R. / Perona, P. / Zisserman, A. | British Library Online Contents | 2007


    TRAFFIC SIGN RECOGNITION DEVICE AND TRAFFIC SIGN RECOGNITION METHOD

    MIYASATO KAZUHIRO / KOYASU TOSHIYA | European Patent Office | 2023

    Free access