Vision-Based Lane Detection Using Hough Transform

ECE 533 Course Project (Digital Image Processing)

Vision-based Lane Detection using Hough Transform

By Zhaozheng Yin

Instructor: Prof.Yu Hen Hu

Dec.12 2003 Preface

Purpose

The purpose of the project component of this course is to demonstrate your ability to apply the knowledge and technique learned during this course.

Types of Projects

Applications -- Applications of image processing to specific research area. In an application project, it should contain the following: (a) explanation of the nature of the application and why image processing is needed, (b) image processing techniques that can be applied to the problem on hand, (c) preliminary results. You should have results ready and report it. Just propose to apply image processing without any result is not acceptable.

Final report content

Introduction Related work Approach Results Summary Reference Appendix Vision-based Lane Detection using Hough Transform

1. Introduction

Lane detection is an important enabling or enhancing technology in a number of intelligent vehicle applications, including lane excursion detection and warning, intelligent cruise control and autonomous driving. Various lane detection methods have been proposed. They are classified into infrastructure-based and vision-based approaches. While the infrastructure-based approaches achieve highly robustness, construction cost to lay leaky coaxial cables or to embed magnetic markers on the road surface is high. Vision based approaches with camera on a vehicle have advantages to use existing lane markings in the road environment and to sense a road curvature in front view. Vision-based location of lane boundaries can be divided into two tasks: lane detection and lane tracking. Lane detection is the problem of locating lane boundaries without prior knowledge of the road geometry. Lane tracking is the problem of tracking the lane edges from frame to frame given an existing model of road geometry. Lane tracking is an easier problem than lane detection, as prior knowledge of the road geometry permits lane tracking algorithms to put fairly strong constraints on the likely location and orientation of the lane edges in a new image. Lane detection algorithms, on the other hand, have to locate the lane edges without a strong model of the road geometry, and do so in situations where there may be a great deal of clutter in the image. This clutter can be due to shadows, puddles, oil stains, tire skid marks, etc. This poses a challenge for edge-based lane detection schemes, as it is often impossible to select a gradient magnitude threshold which doesn’t either remove edges of interest corresponding to road markings and edges or include edges corresponding to irrelevant clutter. Detection of long thick lines, such as highway lane markings from input images, is usually performed by local edge extraction followed by straight line approximation. In this conventional method many edge elements other than lane makings are detected when the threshold of edge magnitude is low, or, in the opposite case, edge elements expected to be detected are fragmented when it is high. This makes it difficult to trace the edge elements and to fit the approximation lines on them.

2. Related work

In recent years, lane detection has been broadly studied and many state-of-the-art systems for detecting and tracking lane/pavement boundaries have shown up. Most lane detection algorithms are edge-based. They relied on thresholding the image intensity to detect potential lane edges, followed by a perceptual grouping of the edge points to detect the lane markers of interest. In many road scenes it isn’t possible to select a threshold which eliminates noise edges without also eliminating many of the lane edge points of interest. University of Michigan’s AI lab uses a test bed vehicle as a platform for data collection and testing. The approach used by Karl kluge looks for a deformable template model of lane structure to locate lane boundaries without thresholding the intensity gradient information. Chris Kreucher introduced a algorithm based the frequency domain features that capture relevant information concerning the strength and orientation of spatial edges. Ohio State University’s Center for Intelligent Transportation Research has also developed two systems: one performs curve identification in the image plane and use heuristic optimization techniques to construct the road geometry, the other used a general image segmentation algorithm to isolate lane marker segments and a voting scheme to unite individual features into lane boundary contours. Toyota Center R&D Labs describes a lane detection method using a real-time voting processor for a driver assist system. For robust lane detection in various environments, they proposed the method based on complete search in a parameter space.

3. Approach

Lane detection is a complicated problem under different light/weather conditions. In this class project we analysis the easy case first: the images are captured from the crossover above the road, assume the lanes to be detected are straight, at daytime and with good weather condition. The lane markings can be solid or dash lines. Other than detecting the lane markers, the mid-line of each lane is also calculated to identify the position of the vehicle with respect to lane makings, which is useful for autonomous driving.

Fig.1 The lane detection algorithm Fig. 1 is the flowchart of the lane detection algorithm, which is based on edge detection and Hough Transform. First the RGB road image is read in and converted into the grayscale image. Then we use the global histogram to find the road background gray and subtract it from the grayscale image to get img1. Edge operation is executed on img1 and lane marking features are preserved in img2. The key technology here is using Hough Transform to convert the pixels in img2 from the image coordinate (x, y) to the parameter space (, ) , and then search in the Hough space to find the long straight lines, which are lane marking candidates. The candidate lines are post-processed: delete the fake ones, select one line from a cluster of closing lines as a lane marking. Finally the lane makings are sorted by their position in the road from left to right. Also the mid-line of each lane is computed to localize the lane. Details of the algorithm are described below:

(1) Find the road background and subtract it from the original image

The ideal case of the road scene is like Fig.2: solid white line is the lane boundary, white dash line is the lane separator and the double yellow solid line is used to separate two driving directions. Due to the perspective transform, the parallel lines in the road scene will converge to the vanishing point in the image.

Fig.2 Ideal road geometry However the road geometry is not so ideal in the real world. For example, in Fig.3(a) the lane boundaries are broken; there are vertical/horizontal scratches or other clutter on the road surface; the vehicles on the road will also affect the detection accuracy of the road geometry. Lane edges are the objects of interest in this work. The features of interest are those that discriminate between lane markings and extraneous (non-lane) edges. Fig. 3(b) is the edge image of Fig.3 (a). Most features of the lane markings are preserved as edges in Fig.3(b), which is directly caused by the edge function of Matlab. But for Fig.4(a), if we use the edge function directly on the original image, much lane marking edge information is lost (Fig.4(b)). Then we consider other preprocess methods to preserve the lane marking information before the edge operation. Background subtraction is a solution. Fig.3 (a) One image of University Ave Fig.3 (b) Edge image of the left image

Fig.4 (a) One image of Loop 4 in Beijing Fig.4 (b) Edge image of the left image Assume most of the pixels in the image belong to the road background. So we consider using the global histogram to find the road surface background gray, as shown in Fig.5. The grayscale which is around the histogram maximum is taken as the background gray.

Fig.5 Global histogram The result image caused by subtracting the background gray from the original image is shown in Fig.6 (a). From the edge image in Fig.6 (b) we can find that the lane marking edge information is preserved.

Fig.6 (a) Subtract background from original image Fig.6 (b) Edge image of the left image

(2) Hough Transform

The Hough transform is used in a variety of related methods for shape detection. These methods are fairly important in applied computer vision; in fact, Hough published his transform in a patent application, and various later patents are also associated with the technique. Here we use it to detect the straight lines. Fig.7 is the fundament idea to convert each pixel in the image to parameter space. We define the origin of the image coordinate as the upper-left point.

Fig.7 Hough transform for detecting straight lines A count array [  ][ ] is constructed for each candidate line and some other array are constructed to record each line’s start/end position. Since the lane markings are not close to the origin and they are not horizontal in the image (for autonomous driving application, the camera is mounted on the vehicle with front view), we only detect the straight lines with restriction   10, 30    150 , and also the calculation time cost is reduced.

(3) Search in the Hough space for the long straight lines There are many straight lines detected by Hough Transform, now we search in the Hough space to find the long straight lines, which are lane marking candidates (shown as red lines in Fig.8). Fig.8 (a) and (b) show the first 20 straight lines of each road, which have the biggest count number, i.e. the lane markings include more edge pixels than other lines in the image.

Fig.8 (a) Candidate lines Fig.8 (b) Candidate lines & fake lines

(4) Decide the lane markings and mid-line of each lane

In Fig.8 there are many lines around the lane markings detected by the Hough Transform, also in Fig.8 (b) some lines, which are caused by the edges of vehicle queues, are counted as straight lines. We need to group the line cluster as one lane marking and delete other fake lines. First we sort the lines according to their position in the image from left to right. Secondly for each line group consisting of closing straight lines, select the most possible line as the lane marking and delete other fake lines (the distance between two lines and their count numbers are used as criteria to judge whether or not this line is a fake lane marking). Finally the mid-line of each lane is calculated from the sorted lane markings. Fig.9 is the detection result for lane markings (shown as red lines) and mid-lines of each lane (shown as green line). From Fig.9 (b) the fake lines caused by the vehicle queue on the road are deleted.

Fig.9 (a) Decide the lane marking Fig.9 (b) Decide the lane marking 4. Results Fig.10 (a) and (b) is the final detection result for Fig.3 (a) and Fig.4 (a). In Fig. 10 (a), the lane markings detected by the algorithm almost match the lane markers in the real world. Because the lane markers in this image are not completely straight and some noise edge information exists around the lane markings, there is a little offset between the detected lines and the lane markings in the world. In Fig. 10 (b) the detected lane markings match the lane markers in the scene very well except the leftmost lane, which is not detected by the edge function.

Fig.10 (a) Detection result of Fig. 3(a) Fig.10 (b) Detection result of Fig. 4(a)

We have shown in Fig.6 (b) that the edge information is preserved by subtracting the road background gray from the original image. Following is an example that restricting the parameter space within a range (ex.   10, 30    150 ) can reduce the bad effect of the road surface scratches. As shown in Fig.11 (a) there are many vertical/horizontal scratches on the road surface and these scratches are evident in the edge image of Fig.11 (b). When there is no limit to the angle  , the scratches will be counted as lane marking candidates (Fig.11 (c)). Otherwise if we restrict the  within a range, the Hough Transform detects the most possible lane marking candidates (Fig.11 (d)). Fig.11 (e) and (f) is the final result.

(a) One image on East University Ave (b) Edge image of left image (c) without restriction to  (d) restrict 

(e) Final result in the edge image (f) final result in the original image Fig. 11 The effect of the scratches Comparing to the above example, Fig.12 is another example with opposite driving direction, and also there are many long horizontal/vertical scratches on the road surface. Through the background subtraction and Hough Transform, we can still detect the lane markings without the bad effect of horizontal/vertical scratches. For the left lane marking in Fig.12 (d), the detected line is a little far away from the lane marking in the scene, because much of the edge information of this lane marking is lost in Fig.12 (b) and Fig.12 (c).

Fig.12 (a) One image on University Ave. Fig.12 (b) Hough Transform Fig.12 (c) final result Fig.12 (d) final result

5. Summary From the above result, we find the algorithm works well for these cases. The key method includes: find the background gray range, background subtraction, edge detection, Hough Transform, find the long lane marking candidates, sort the lane marking candidates, group the cluster lines as one line, delete fake lines and calculate the mid-line of each lane. Since the algorithm is edge-based, it’s sensitive to the edge information, just like the example of Fig.12 (d). To improve the robustness of the algorithm, we can consider some other methods in the future, like: deformable- template, multi-resolution Hough Transform, B-snake, multi-sensor fusion.

Reference 1. Karl Kluge, Sridhar Lakshmanan, “A deformable-template approach to lane detection”, Intelligent Vehicles '95 Symposium., Proceedings of the, 25-26 Sept. 1995 Page(s): 54 -59 2. Kreucher, C.; Lakshmanan, S.; “A frequency domain approach to lane detection in roadway ima”, Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on , Volume:2, 24-28 Oct.1999 Page(s): 31 -35 vol.2 3. Gonzalez, J.P.; Ozguner, U.; “Lane detection using histogram-based segmentation and decision trees”, Intelligent Transportation Systems, 2000. Proceedings. 2000 IEEE , 1-3 Oct. 2000 Page(s): 346 -351 4. Bertozzi, M.; Broggi, A.; “Real-time lane and obstacle detection on the GOLD system”, Intelligent Vehicles Symposium, 1996., Proceedings of the 1996 IEEE , 19-20 Sept. 1996 Page(s): 213 -218 5. Bertozzi, M.; Broggi, A.; “GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection”, Image Processing, IEEE Transactions on , Volume: 7 Issue: 1 , Jan.1998 Page(s): 62 -81 6. Yu, B.; Jain, A.K.; “Lane boundary detection using a multiresolution Hough transform”, Image Processing, 1997. Proceedings., International Conference on , 26-29 Oct. 1997 Page(s): 748 -751 vol.2 7. Yue Wang; Eam Khwang Teoh; Dinggang Shen; “Lane detection using B-snake”, Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on , 31 Oct.-3 Nov. 1999 Page(s): 438 -443 8. Ma, B.; Lakahmanan, S.; Hero, A.; “Road and lane edge detection with multisensor fusion methods”, Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on , Volume: 2 , 24-28 Oct. 1999 Page(s): 686 -690 vol.2 9. Ma, B.; Lakshmanan, S.; Hero, A.O., III; “Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion”, Intelligent Transportation Systems, IEEE Transactions on , Volume: 01 Issue: 3 , Sept. 2000 Page(s): 135 -147 10. Takahashi, A.; Ninomiya, Y.; Ohta, M.; Tange, K.; “A robust lane detection using real-time voting processor”, Intelligent Transportation Systems, 1999. Proceedings. 1999 IEEE/IEEJ/JSAI International Conference on , 5-8 Oct. 1999 Page(s): 577 -580 Appendix

One output sample: