Real-time illumination-invariant speed-limit sign recognition based on a modified census transform and Support Vector Machines

Kwangyong Lim, Taewoo Lee, Changmok Shin, Soonwook Chung, Yeongwoo Choi, Hyeran Byun

Research output: Contribution to conferencePaper

3 Citations (Scopus)

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.

Original languageEnglish
DOIs
Publication statusPublished - 2014
Event8th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2014 - Siem Reap, Cambodia
Duration: 2014 Jan 92014 Jan 11

Other

Other8th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2014
CountryCambodia
CitySiem Reap
Period14/1/914/1/11

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Lim, K., Lee, T., Shin, C., Chung, S., Choi, Y., & Byun, H. (2014). Real-time illumination-invariant speed-limit sign recognition based on a modified census transform and Support Vector Machines. Paper presented at 8th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2014, Siem Reap, Cambodia. https://doi.org/10.1145/2557977.2558090