UW-Madison ECE 533 - Vision-based Lane Detection using Hough Transform (12 pages)

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Vision-based Lane Detection using Hough Transform



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Vision-based Lane Detection using Hough Transform

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Pages:
12
School:
University of Wisconsin, Madison
Course:
Ece 533 - Image Processing
Image Processing Documents
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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


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