Vision-based Lane Detection using Hough Transform By: Zhaozheng Yin Instructor: Prof. Yu Hen Hu Dec

Introduction Application of lane detection : 1. Lane excursion detection and warning 2. Intelligent cruise control 3. Autonomous driving … Some lane detection algorithms Edge-based, Deformable-template, B- snake…

Approach (Edge-based) Step 1: Get the edge information Step 2: Hough Transform Step 3: Search out the lane marking candidates Step 4: Decide the lane marking

Approach Step 1: Get the edge information Lost many edges

Approach Step 1: Get the edge information (cont.) Use global histogram to find the background gray range and subtract it from the original image Edge operation Compare these images Edges are preserved using background subtraction method

Approach Step 2: Hough Transform 1.An array is used to count how many pixels belong to the line through Hough Transform. 2.Another restriction is added: Avoid detecting the fake lane markings

Approach Step 3: Search out the lane marking candidates University Ave. Loop 4 in Beijing 1.The red lines are the first 20 lines which have the biggest count numbers in Hough parameter space. 2.For each lane marking in the real scene, there are many line candidates around it. 3.There are some fake lines caused by the vehicle queue.

Approach Step 4: Decide the lane marking 1. Sort the candidate lines by their position from left to right 2. Around each line cluster, choose the candidate which has the biggest count number as the lane marking in real scene 3. Delete the fake lane marking candidates 4. Calculate the mid-line of each lane (shown as green lines)

Result There is a little offset between the detected lane marking and that in real scene. This is because the lane is not completely straight and the lane mark is broken in the scene. This is a nice result

Discussion (effect of scratches 1) University Ave.Edge image Without restriction to RestrictDecide the lane markingsDetected lane markings

Discussion (effect of scratches 2) University Ave. Restrict Decide the lane markingsDetected lane markings Note: Because lots of the edge information for the left lane marking are lost, there is an offset between the detected lane marking and that in the real scene

Summary Alg. works well for these straight lane cases. Key methods includes: Find the background gray range, background subtraction, edge detection, Hough Transform, find the 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 More complicate case (future work) Consider other methods, like deformable-template, multi-resolution Hough Transform, B-snake, multi-sensor fusion

Reference Karl Kluge, Sridhar Lakshmanan, “A deformable- template approach to lane detection”, Gonzalez, J.P.; Ozguner, U.; “Lane detection using histogram-based segmentation and decision trees”, Yu, B.; Jain, A.K.; “Lane boundary detection using a multiresolution Hough transform”, Yue Wang; Eam Khwang Teoh; Dinggang Shen; “Lane detection using B-snake”, Others