ROAD BOUNDARIES DETECTION USING COLOR SATURATION Pierre CHARBONNIER*, Philippe NICOLLE**, Yannick GUILLARD* and Jean CHARRIER** Laboratoire Regional des Ponts et Chaussees de Strasbourg,
11 Rue Jean Mentelin, B.P. 9, 67035 STRASBOURG cedex, FRANCE e-mail:
[email protected] Laboratoire Central des Ponts et Chaussees,B.P. 19, 44340 BOUGUENAIS, FRANCE e-mail:
[email protected]
ABSTRACT
Our aim is to automatically detect the road borders in a road scene image. This is useful to many road scenes analysis applications, in both elds of vehicle guidance and civil engineering. Diculties arise because pavements are often heterogeneous and because illumination variations often occur in outdoor scenes. Some vehicle navigation projects use colour images for road borders detection [1, 3], but most of the time they consider RGB features, which are sensitive to shadows and pavement defects. Besides, the proposed classi cation methods are often computationally intensive [7]. In contrast, chromatic saturation is a discriminant oneband feature, so a simple threshold can be used, making the classi cation quite fast. Moreover, chromatic saturation is quite insensitive to shadows and pavement variability.
1 INTRODUCTION
The aim of this paper is to propose a new unsupervised approach for automatic road boundary detection in road scenes, using colour information. To know which part of an image corresponds to the roadway is of great interest in many applications. Wellknown ones are vehicle guidance and obstacle detection. Several civil engineering applications, such as road width measurement, pavement defects diagnosis, road markings repainting [4], or location of objects on the roadside, also need road boundaries detection. In these applications, we have to deal with possibly unstructured roads. Therefore, we cannot rely on road markings. Segmenting the image into two classes: "road" and "o road" is dicult. Firstly because the road object is heterogeneous. This can be due to a di erence of materials composing the pavement, especially when the road has been repaired, or in the presence of defects, cracks, dirt, or road markings. Secondly, we have to cope with realworld illumination conditions (see example on g. 1). All these variations a ect the luminance of pixels, making grey-scale image analysis unsuited. It is important to notice that road pavements are often poorly chromatic. This is also the case for road mark-
ings, repairs, cracks, earth and dirt. Conversely, roadsides, especially in non-urban areas, are often grassy, thus coloured. Therefore, colour seems to be an interesting feature for road detection under the assumption that there is a chromatic contrast between pavement and roadsides. All that will be developed in this paper is based on this assumption. Some vehicle navigation projects involve colour images for road borders detection [3, 1], but most of the time they use RGB features, that are sensitive to luminance variations. This may lead to over-segmentation, and a post-processing is often needed. A better way is to consider chromatic coecients [4], that represent the proportion of each fundamental colour in the pixel: r = R=(R + G + B ); g = G=(R + G + B ); b = B=(R + G + B ). However, a three-band classi cation method is still needed. As stated in [7], this can be computationally intensive, even if a neural network classi cation is used, as in [4]. Chromatic saturation is a measure of the proportion of white light in a given colour. Grey pixels have a low chromatic saturation, while coloured ones are quite saturated. It is insensitive to luminance variations, it can be computed eciently using Look-Up Tables (LUT's). It is a single-band discriminant feature for roadway detection,allowing a low-computation cost detection algorithm.
2 CHROMATIC SATURATION
Several colour representation systems involve a measure of chromatic saturation. We use the Hue-Saturationintensity (HSI) formulation [2, 5]. The chromatic saturation, S , of a pixel represented by its three colour components R, G and B , is: Min(R; G; B ) S = 1 Average (R; G; B ) It is possible to pre-compute S for every value of the sum and the minimum of R, G and B and to store it in a Look-Up Table. The on-line computation of S then only requires two additions and two tests. Note that the previous expression means that: S = 1 3 Min(r; g; b).
This shows that it is not necessary to use three chromatic coecients as in [4]. When a pixel is grey, the values of r, g and b are close to 33%, so the saturation is close to 0. At the contrary, a pure colour is such that at least one of the three chromatic coecients is equal to zero; i.e. S is equal to one. This makes chromatic saturation an ecient feature for roadway segmentation, in our hypothesis. Since S involves a proportion of colour, it is independent from luminance, as it can be seen on g. 1. The pavement appears in dark grey while the road borders are lighter. Besides, the segmentation is reduced to a one-dimensional problem.
3 PROPOSED METHOD Firstly, the chromatic saturation image is computed. In order to make image sequence analysis faster, the processing can be limited to areas of interest (AOI's), centred on road borders (see g. 2). Their position and size are updated each time detection is performed. In the second step of the method, the "road" - "o road" classi cation is made by a simple thresholding technique. There are several possibilities to compute the threshold. We perform a scale space analysis of the chromatic saturation histogram, as in [6] until only two clusters are present. The one with lower saturation corresponds to the pavement and the other one to the "o road" class. When the assumption of chromatic contrast between pavement and roadside is not veri ed, only one cluster is detected. The process can be interrupted, and a warning can be emitted to the system. The last step consists of extracting road boundaries from the segmented image. In image sequence processing, a prediction of borders positions can be used to discard too far border points, that might correspond to misclassi cations. A robust regression technique, nmely median least squares [8], is then used to approximate the boundaries by a segment.
4 EXPERIMENTAL RESULTS We present here experimental results obtained with a road width measurement program, currently being developed at the Laboratoire Central des Ponts-etChausses. The project concerns secondary roads, where the chromatic contrast assumption is often veri ed. Images are acquired on an ICPCI card with a colour module. For the moment we do not use any speci c hardware for computations. The application runs on a Pentium 100MHz PC. The process is limited to two areas of interest (AOI's). We obtain a processing rate of 2 images per second. Figure 3 shows the application window of the road width measurement program. The AOI's are shown as dotted black rectangles. The extracted border points and the approximated borders are also drawn. The horizontal
width measurement line is only drawn when both left and right road borders have been detected. It can be seen on g. 4 and 5 that we obtain satisfying results, despite the bad repair of the road, and even in presence of shadows. On gure 4, we remark that no border was detected on the right side. This is because there is no real chromatic contrast between pavement and road. In that case, anyway, colour information is not useful and other attributes should be used.
5 CONCLUSION
We propose a road border detection method based on chromatic saturation that measures the quantity of grey in a pixel. Because this feature does not depend on the luminance of pixels, the method is quite robust to both pavement and illumination variations. Last, it is easy to implement and quite fast. The method has been tested in a road width measurement application. It gives very satisfactory results. It is now being combined with a geometric road - vehicle model and a Kalman ltering in order to increase the precision of the width measurement.
References
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Figure 1: RGB image (left), luminance (center) and chromatic saturation (right)
Figure 2: extracted AOI (left) and its chromatic saturation histogram (right)
Figure 3: road width measurement application
Figure 4: example of a repaired road
Figure 5: the right border is not detected because there is no chromatic contrast between road and roadside
