Video-based traffic sign detection, tracking, and recognition is one of the important components for the intelligent transport systems. Extensive research has shown that pretty good performance can be obtained on public data sets by various state-of-the-art approaches, especially the deep learning methods. However, deep learning methods require extensive computing resources. In addition, these approaches mostly concentrate on single image detection and recognition task, which is not applicable in real-world applications. Different from previous research, we introduce a unified incremental computational framework for traffic sign detection, tracking, and recognition task using the mono-camera mounted on a moving vehicle under non-stationary environments. The main contributions of this paper are threefold: 1) to enhance detection performance by utilizing the contextual information, this paper innovatively utilizes the spatial distribution prior of the traffic signs; 2) to improve the tracking performance and localization accuracy under non-stationary environments, a new efficient incremental framework containing off-line detector, online detector, and motion model predictor together is designed for traffic sign detection and tracking simultaneously; and 3) to get a more stable classification output, a scale-based intra-frame fusion method is proposed. We evaluate our method on two public data sets and the performance has shown that the proposed system can obtain results comparable with the deep learning method with less computing resource in a near-real-time manner.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An Incremental Framework for Video-Based Traffic Sign Detection, Tracking, and Recognition


    Contributors:
    Yuan, Yuan (author) / Xiong, Zhitong (author) / Wang, Qi (author)


    Publication date :

    2017-07-01


    Size :

    3057802 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    COUPLED DETECTION, ASSOCIATION AND TRACKING FOR TRAFFIC SIGN RECOGNITION

    Boumediene, M. / Lauffenburger, J. / Daniel, J. et al. | British Library Conference Proceedings | 2014


    Automatic video processing for traffic sign recognition

    Allasia, W. / Culeddu, C. / Gallo, F. et al. | Tema Archive | 2009


    Traffic Sign Detection and Recognition

    Pillai, Preeti S. / Kinnal, Bhagyashree / Pattanashetty, Vishal et al. | Springer Verlag | 2022