Real-time illumination-invariant speed-limit sign recognition based on a modified census transform and support vector machines

Authors:
Kwangyong Lim;Taewoo Lee;Changmok Shin;Soonwook Chung;Yeongwoo Choi;Hyeran Byun
Affiliations:
Yonsei University, Seodaemun-Gu, Seoul, Korea;Yonsei University, Seodaemun-Gu, Seoul, Korea;Hyundai Mobis Co., Ltd., Mabuk-Ro, Giheung-Gu Yongin-Si, Gyeonggi-Do, Korea;Hyundai Mobis Co., Ltd., Mabuk-Ro, Giheung-Gu Yongin-Si, Gyeonggi-Do, Korea;Women's University, Chungpa, Yongsan-Gu, Seoul, Korea;Yonsei University, Seodaemun-Gu, Seoul, Korea
Venue:
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
Year:
2014

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Abstract

In this paper, we propose a robust illumination system for speed-limit sign recognition in real-time. Real-time traffic sign detection with various illuminations is one of the challenges in a vision-based intelligent vehicle system, as illumination varies greatly in real-world road images based on factors such as driving time, weather, lighting conditions, and driving directions. Our method uses a MCT (Modified Census Transform) as an illumination-invariant method for the real-time detection of traffic signs and uses a SVM (Support Vector Machine) as a classifier for detection and validation. With the proposed method, we have obtained a very high detection rate of 99.8% and recognition rates of 98.4% on various real-world driving images.