Abstract
This paper aims to deal with real-time traffic sign detection. To this end, a two-stage method is proposed to reduce the processing time with little influence to AUC (area under curve) value. In first stage, a color probability model is proposed to transform an input image to probability maps. The traffic sign proposals are then extracted by finding maximally stable extremal regions on these maps. In second stage, an integral channel features detector is employed to remove false positives of the proposals. Experiments on the GTSDB benchmark [1] show that the proposed color probability model achieves the highest recall rate and the proposed two-stage method significantly improves computational efficiency with good AUC value in comparison with the state-of-the-art methods.
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Yang, Y., Wu, F. (2014). Real-Time Traffic Sign Detection via Color Probability Model and Integral Channel Features. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_58
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DOI: https://doi.org/10.1007/978-3-662-45643-9_58
Publisher Name: Springer, Berlin, Heidelberg
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