Open AccessFinely Crafted Features for Traffic Sign Recognition
Author(s) -
Wei Li,
Haiyu Song,
Pengjie Wang
Publication year - 2022
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
ISSN - 1998-4464
DOI - 10.46300/9106.2022.16.20
Subject(s) - traffic sign recognition , discriminative model , computer science , artificial intelligence , feature extraction , feature (linguistics) , pattern recognition (psychology) , feature vector , traffic sign , sign (mathematics) , key (lock) , computer vision , dimension (graph theory) , advanced driver assistance systems , machine learning , mathematics , mathematical analysis , linguistics , philosophy , computer security , pure mathematics
Traffic sign recognition (TSR) is the basic technology of the Advanced Driving Assistance System (ADAS) and intelligent automobile, whileas high-qualified feature vector plays a key role in TSR. Therefore, the feature extraction of TSR has become an active research in the fields of computer vision and intelligent automobiles. Although deep learning features have made a breakthrough in image classification, it is difficult to apply to TSR because of its large scale of training dataset and high space-time complexity of model training. Considering visual characteristics of traffic signs and external factors such as weather, light, and blur in real scenes, an efficient method to extract high-qualified image features is proposed. As a result, the lower-dimension feature can accurately depict the visual feature of TSR due to powerful descriptive and discriminative ability. In addition, benefiting from a simple feature extraction method and lower time cost, our method is suitable to recognize traffic signs online in real-world applications scenarios. Extensive quantitative experimental results demonstrate the effectiveness and efficiency of our method.