Traffic Sign Recognition Based on Vision Bionics

Abstract:

Inspired by the research of human visual system in neuroanatomy and psychology, the paper proposes an road traffic sign identification model based on vision bionics.The model combines data-driven and task-driven visual attention mechanism to focus on traffic sign target rapidly and accuractly.Firstly,It simulates the Itti attention model to obtain “what” information and uses the priori knowledge of positional distribution of traffic sign as “where” information.Then,it adjusts saliency map according to “what” and “where” stream so as to select traffic sign focus preferentially.Secondly,it measures the similarity of shape and color features between traffic sign and attention region to get interested region.Finally, it segments traffic sign based on color characteristics and classify shape of traffic sign based on the Support Vector Machine method. The experimental results demonstrate that the feasibility and effectiveness of the proposed model; Furthermore, the average accuracy rate of shape classification on DtBs matrix reaches 98%.

• Periodical: Applied Mechanics and Materials (Volumes 190-191)
• Pages: 746-751
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.190-191.746
• Online since: July 2012
• Authors: Zhuo Zhang, Xin Nan Fan, Xue Wu Zhang, Rui Yu Liang, Shan Ming Lin
• Keywords: Selective Attention, Support Vector Machine (SVM), Traffic Sign Recognition, Vision Bionics

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