On-road speed sign recognition using fuzzy kernel-based learning vector quantization

This paper presents an automatic speed sign recognition for providing the visual driving-assistance of speed limits awareness. To reduce the influence of digital noise caused by lighting condition and pollution, a segmentation based on pan-red color information is applied to extract the shape of speed sign. Based on the edge gradient information of a circle shape, a radially symmetry detection strategy is proposed for fast detecting the speed sign candidate from road scenes. The recognition of the content of speed sign is achieved through the fuzzy kernel-based learning vector quantization (FKLVQ) which also verifies each candidate to eliminate non-target blobs. Results show the feasibility and effectiveness of the proposed system under a wide variety of visual conditions.