Road Detection

The proposed feature point extraction technique is integrating a road detection procedure [26] which is used an on-line color model that we can train an adaptive color model to fit road color. The main objective of road detection is to discriminate the road and non-road region roughly, because the result is used to support feature extraction not used to extract obstacle regions. However, we adopt an on-line learning model that allows continuously update during driving, through the training method that can enhance plasticity and ensure the feature is on the road region.

Due to the color appearance in the driving environment, we have to select the color features and using these color features to build a color model of the road.

Therefore, we have to choose a color space which has uniform, little correlation, concentrated properties in order to increase the accuracy of the model. In computer color vision, all visible colors are represented by vectors in a three-dimensional color

26

space. Among all the common color spaces, RGB color space is the most common color feature selected because it is the initial format of the captured image without any distortion. However, the RGB color feature is high correlative, and the similar colors spread extensively in the color space. As a result, it is difficult to evaluate the similarity of two color from their 1-norm or Euclidean distance in the color space.

The other standard color space HSV is supposed to be closer to the way of human color perception. Both HSV and L*a*b* resist to the interference of illumination variation such as the shadow when modeling the road area. However, the performance of HSV model is not as good as L*a*b* model because the road color cause the HSV model not uniform that lead to the HSV color model not as uniform as the L*a*b* color model. There are many reasons attribute this result. Firstly, HSV is very sensitive and unstable when lightness is low. Furthermore, the Hue is computed by dividing (Imax - Imin) in which Imax = max(R,G,B), Imin = min(R,G,B), therefore when a pixel has a similar value of Red, Green and Blue components, the Hue of the pixel may be undetermined. Unfortunately, most of the road surface is in similar gray colors with very close R, G, and B values. If using HSV color space to build road color model, the sensitive variation and fluctuation of Hue will generate inconsistent road colors and decrease the accuracy and effectiveness. L*a*b* color space is based on data-driven human perception research that assumes the human visual system owing to its uniform, little correlation, concentrate characteristics are ideally developed for processing natural scenes and is popular for color-processed rendering.

L*a*b* color space also possesses these characteristics to satisfy our requirement. It maps similar colors to the reference color with about the same differences by Euclidean distances measure and demonstrates more concentrated color distribution than others. Then considering the advantaged properties of L*a*b* for general road environment, the L*a*b* color space for road detection is adopted.

27

The RGB-L*a*b* conversion is described as follow equations:

1. RGB-XYZ conversion:

where Y Z are tristimulus values of reference white poi

⎧ ⎛ ⎞

By modeling and updating of the L*a*b* color model, the built road color model can be used to extract the road region. The L*a*b* model is constituted of K color balls, and each color ball mi is formed by a center on ( ,* ,* )

i i i

m m m

L a b and a fixed radiusλ_max =5 as seen in Fig. 4-4. In order to train a color model, we set a fixed area of the lower part of the image and assume pixels in the area are the road samples. For each of these pixels in the beginning 30 frames are used to initialize the color model, and updating the model every ten frames to increase processing speed but still maintain high accurate performance.

28

Fig. 4-4 A color ball i in the L*a*b* color model whose center is at (Lm, *am, *bm) and with radius λ _max

The sampling area is used to be modeled by a group of K weighted color balls.

We denote the weight and the counter of the mi th color ball at a time instant t by _,

m ti

W

and _,

m ti

Counter , and the weight of each color ball represents the stability of the color.

The color ball which more on-line samples belonged to over time accumulated a bigger weight value shown in Fig. 4-5. Adopting the weight module increases robustness of the model.

Fig. 4-5 Sampling area and color ball with a weight which represents the similarity to current road color.

29

The weight of each color ball is updated by its counter when the new sample is coming which is called one iteration. Therefore the counter would be initialized to zero at the beginning of iteration. The counter of each color ball records the number of pixels added from the on-line samples in the iteration. The first thing to do is that which color ball is chosen to be added. We measure the similarity between new pixel xt and the existing K color balls using a Euclidean distance measure (4-1). The maximum value of K is 50 which represents each on-lined model contains 50 color balls at most. added to the counter of best matching color at this iteration as the equation (4-2).

After entire new sample pixels at this iteration undertake the matching procedures mentioned above, the weights of every color ball are updated according to their current counter and their weight at last iteration. The updating method is as follows:

i mi i i max

, where α is user-defined learning rate, N_w sample is the sampling area

Then using the weight to decide which color ball of the model most adapt and resemble current road. The color balls are sorted in a decreasing order according to their weights. As a result, the most probable road color features are at the top of the list. The first B color balls are selected to be enabled as standard color for road detection, and these color balls with a higher weight has more importance in detection

step

4.1

32

Fig. 4-8 Results of feature point extraction. The upper image is result of road detection, and lower image is position of feature points.