# Fusing Appearance and Prior Cues for Road Detection
## 1 Introduction
The former usually finds the drivable area by detecting the texture features of the road. In [15; 16], the vanishing point was detected followed by the segmentation of the corresponding road surface. The main contribution of the approach is a novel adaptive soft voting scheme using Gabor filters, which compute the dominant texture orientation at each pixel. In [17], the road was detected by finding the boundaries of the road surface. For a structured road environment, the input images have clear lane markings and some algorithms detect the drivable area by extracting the lane markings on the road surface [18; 19]. However, texture in the road images varies a lot with the distance to the camera mounted on the vehicle due to the perspective effect. Moreover, lots of texture features such as vanishing point and lane markings will be covered when there are vehicles and pedestrians on the road. On the contrary, color offers more powerful and stable information about the road over texture. Thus, in this paper we use color information as the main appearance cue for road detection. Unfortunately, conventional color based methods [20; 21; 22; 23; 24] usually fail under varying illumination conditions especially when there are strong shadows or highlights. The strong point of these algorithms is that they can provide straightforward information about the free road area without any training. To this end, [PERSON] et al. [25] proposed a road detection algorithm which uses the illuminant invariance theory on RGB images to find road pixels via a histogram correlation method where RGB means red, green and blue channels in color images. Its robustness to varying illumination conditions shows a significant improvement in performance compared to previous color based methods when there are shadows and highlights in the input images. However, the illuminant invariant images lost lots of detail information, which is to the disadvantage of road detection. In addition, color based road detection algorithms usually fail when in the presence of sidewalks, buildings, and overpasses which are made of similar material to the road surface. In order to improve the precision of road detection, some researchers use additional sensors to find the drivable area, such as stereo vision [26; 27], LIDAR (Light Detection And Ranging) [28; 29], global positioning systems (GPS), and digital maps [30]. Comprehensive evaluations showed that these approaches can be adapted in various driving conditions and rank high, with good performances achieved in the KITTI road benchmark. Nevertheless, a drawback of these algorithms is that they are too expensive to be applied in a commercial environment. In this paper, we propose a novel method for road detection using monocular color images. Firstly, we segment the input image into superpixels. Meanwhile, morphological processing and illuminant invariant transformation are conducted on the input image. Then, we design a novel seed superpixels selection method and model appearance cues online using the Gaussian mixture model with the selected seed superpixels. Finally, we find road pixels using a Bayesian framework to fuse the appearance model and geometric prior model constructed offline. The rest of this paper is organized as follows. Section 2 describes an overview of our proposed method. Experimental results and discussion are presented in Section 3, followed by the conclusion and future work in Section 4.