TY - GEN
T1 - Road sign detection using eigen color
AU - Tsai, Luo Wei
AU - Tseng, Yun Jung
AU - Hsieh, Jun-Wei
AU - Fan, Kuo Chin
AU - Li, Jiun Jie
PY - 2007/12/1
Y1 - 2007/12/1
N2 - This paper presents a novel color-based method to detect road signs directly from videos. A road sign usually has specific colors and high contrast to its background. Traditional color-based approaches need to train different color detectors for detecting road signs if their colors are different. This paper presents a novel color model derived from Karhunen-Loeve(KL) transform to detect road sign color pixels from the background. The proposed color transform model is invariant to different perspective effects and occlusions. Furthermore, only one color model is needed to detect various road signs. After transformation into the proposed color space, a RBF (Radial Basis Function) network is trained for finding all possible road sign candidates. Then, a verification process is applied to these candidates according to their edge maps. Due to the filtering effect and discriminative ability of the proposed color model, different road signs can be very efficiently detected from videos. Experiment results have proved that the proposed method is robust, accurate, and powerful in road sign detection.
AB - This paper presents a novel color-based method to detect road signs directly from videos. A road sign usually has specific colors and high contrast to its background. Traditional color-based approaches need to train different color detectors for detecting road signs if their colors are different. This paper presents a novel color model derived from Karhunen-Loeve(KL) transform to detect road sign color pixels from the background. The proposed color transform model is invariant to different perspective effects and occlusions. Furthermore, only one color model is needed to detect various road signs. After transformation into the proposed color space, a RBF (Radial Basis Function) network is trained for finding all possible road sign candidates. Then, a verification process is applied to these candidates according to their edge maps. Due to the filtering effect and discriminative ability of the proposed color model, different road signs can be very efficiently detected from videos. Experiment results have proved that the proposed method is robust, accurate, and powerful in road sign detection.
UR - http://www.scopus.com/inward/record.url?scp=38149048554&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-76386-4_15
DO - 10.1007/978-3-540-76386-4_15
M3 - Conference contribution
AN - SCOPUS:38149048554
SN - 9783540763857
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 169
EP - 179
BT - Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
Y2 - 18 November 2007 through 22 November 2007
ER -