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Vision-based Lane Detection using Hough Transform. By: Zhaozheng Yin Instructor: Prof. Yu Hen Hu Dec.12 2003. Introduction. Application of lane detection : 1. Lane excursion detection and warning 2. Intelligent cruise control 3. Autonomous driving … Some lane detection algorithms

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vision based lane detection using hough transform

Vision-based Lane Detection using Hough Transform

By: Zhaozheng Yin

Instructor: Prof. Yu Hen Hu

Dec.12 2003

introduction
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
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
Approach
  • Step 1: Get the edge information

Lost many edges

approach1
Approach
  • Step 1: Get the edge information (cont.)

Use global histogram to find the background gray

range and subtract it from the original image

Compare these images

Edges are preserved

using background

subtraction method

Edge operation

approach2
Approach
  • Step 2: Hough Transform
  • An array is used to count how
  • many pixels belong to the line through
  • Hough Transform.
  • Another restriction is added:
  • Avoid detecting the fake lane markings
approach3
Approach
  • Step 3: Search out the lane marking candidates

University Ave. Loop 4 in Beijing

  • The red lines are the first 20 lines which have the biggest count numbers in Hough parameter space.
  • 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.
approach4
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
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
Discussion (effect of scratches 1)

University Ave.

Edge image

Without restriction to

Restrict

Decide the lane markings

Detected lane markings

discussion effect of scratches 2
Discussion (effect of scratches 2)

University Ave.

Restrict

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

Decide the lane markings

Detected lane markings

summary
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
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