Illumination invariant color segmentation method based on cluster center tree for traffic sign detection

This paper proposes a color segmentation method that can locate candidate regions of traffic signs accurately and reliably from real world images. In the real world, there are various light conditions which make the color segmentation very difficult problem. Hence, we propose an illumination invariant color segmentation method. The proposed method consists of two parts; 1) cluster center treebased segmentation 2) illumination estimation. Cluster center tree is trained for color segmentation. Illumination estimation algorithm classifies light condition of the input images. We validate the proposed method qualitatively and quantitatively with 1,745 images containing red and blue traffic signs captured with four light conditions; sunny, cloudy, rainy and night. The proposed method achieves the high detection rate of 99.25% in sunny, 98.33% in cloudy, 87.85% in rainy and 88.70% at night.