Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
72. Hierarchical Sparse Representation for Traffic Sign Recognition
verfasst von : Yaxiang Fan, Hao Sun, Shilin Zhou, Huanxin Zou
Erschienen in: Proceedings of 2013 Chinese Intelligent Automation Conference
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Researchers have proposed various machine learning algorithms for traffic sign recognition (TSR), which is a supervised multicategory classification problem with unbalanced class frequencies and various appearances. This paper presents a novel framework for traffic sign recognition exploiting the sparse property of intrinsic information of traffic sign. The contributions of our work are twofold: on one hand, the intrinsic discriminating information among different categories is utilized, on the other an efficient hierarchical sparse representation classification (HSRC) strategy is adopted. Experiments on publicly available datasets show that HSRC is efficient for traffic sign recognition, achieving higher accuracy than many state-of-the art schemes.