title: Real-time traffic sign recognition based on a general purpose GPU and deep-learning.
abstract: We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).
authors.affiliation: Department of Computer Science, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, Republic of Korea.
authors.forename: Kwangyong
authors.identifier: http://orcid.org/0000-0003-1305-5820
authors.initials: K
authors.lastname: Lim
authors.affiliation: Department of Computer Science, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, Republic of Korea.
authors.forename: Yongwon
authors.initials: Y
authors.lastname: Hong
authors.affiliation: Department of Computer Science, Sookmyung Women's University, 47 Cheongpa-ro Yongsan-gu, Seoul, Republic of Korea.
authors.forename: Yeongwoo
authors.initials: Y
authors.lastname: Choi
authors.affiliation: Department of Computer Science, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul, Republic of Korea.
authors.forename: Hyeran
authors.initials: H
authors.lastname: Byun
mesh_terms: D000465:Algorithms; D001334:Automobile Driving; D006801:Humans; D007858:Learning; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated