# Vanishing point detection for road detection

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5 6 V 6 Figure 7. Illustration of detection of the two most dominant edges. Top left: line segments consisting of discrete oriented points. Top right: initially detected vanishing point. Bottom left: detection of the two most dominant edges based on initial vanishing point. Bottom right: the two most dominant edges and updated vanishing point. vanishing point as a constraint to find the first most dominant edge of the road. The top right image of Fig.7 illustrates this search process, where the first most dominant edge is detected as the one which has the largest CR from the set of lines going through the initial vanishing point (the angle between two neighboring lines is set to be 5 ). The red line, E, in the bottom left image of Fig.7, is detected as the first most dominant edge and its length is denoted as. To avoid possible false detection caused by short edges, the smallest is set to be the half image height. nce the first border of the road E is found, we will update the initial vanishing point by looking at the points on E having several dominant edges converging to it according to the CR. For this, through each (regularly) sampled pixel on E, we construct a set of line segments ( ) such that the angle between any two neighboring of is fixed (. X in our experiments). We also set the angle between E and any one of is larger than 20 (motivated by the assumption that the vanishing angle between the two road borders is larger than 20 ). We compute the CR for each line of, and for each new vanishing point candidate, # we consider the sum of the top CR ( in our experiments). The green line segments in Fig.7 are the lines starting from receiving the top CR. The vanishing point is then estimate as the point maximizing. We try other points along E besides the initial vanishing point since the initial vanishing point estimation may not be accurate (i.e., it is not the joint point of the most dominant edges of the roads). The updated initial vanishing point estimate can be observed in the last three columns of the last five Table 1. Selection criterion for the second dominant edge 1. Counting the number of dominant edges which deviate to left and right respectively 2. If all deviate to left or right, the two most dominant edges correspond to the two candidates with the largest and smallest deviation angle respectively. 3. therwise, find those dominant edges which have different deviation orientation from the first dominant edge. Divide these dominant edges into several clusters according to the angle between two neighboring dominant edges, e.g., if the angle is no smaller than Y., the two neighboring dominant edges belong to different clusters. 5. Find the center of the largest cluster as the deviation angle of the second most dominant edge. If more than one clusters have equal number of dominant edges, the center of these clusters is used. rows of Fig.10. From the updated vanishing point and more precisely from the dominant edges which have voted for it, we deduce the position of the second border of the road as explained in Table 1. The length of the obtained second most dominant edge is denoted 9 and the length of the first dominant edge is updated to (see Fig.7). The smallest and the smallest 9 are set to be one third of the image height to avoid possible false detections. 6. Experimental results 6.1. Vanishing point estimation Vanishing point estimation is tested on 1003 general road images. These road images exhibit large variations in color, texture, illumination and ambient environment. Among them, about 30 images are from the photographs taken on a scouting trip along a possible rand Challenge route in the Southern California desert and the other part is downloaded from internet by oogle Image. Some image samples are shown in Fig.1. All images are normalized to the same size with height of 180 and width of 20. To assess the algorithm s performance vs. human perception of the vanishing point location, we request 5 persons to manually mark the vanishing point location after they are trained to know the vanishing point concept. A median filter is applied to these human recorded results and the average of the median filter results is regarded as the ground truth position. For brevity, the soft voting strategy defined in Eq.2 is denoted by Soft and the hard voting strategy (by replacing with 1 in Eq.2)is denoted as Hard. The % M ) voting strategy based on global voting region (left image of Fig.6) is denoted by lobal and the one based on local voting region (right image of Fig.6) is denoted by Local.

8 (a) (b) Figure 10. Vanishing point estimation and dominant edge detection. [16] S.-. T. Tsung-Ying Sun and V. Chan. Hsi color model based lane-marking detection. IEEE Intelligent Transportation Systems Conference, [17] Y. Wang, E. K. Teoh, and D. Shen. Lane detection and tracking using b-snake. Image and Vision Computing, 200. [18] B. Yu and A. K. ain. Lane boundary detection using a multiresolution hough transform. ICIP, 1997.

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