Distance Determination for an Automobile Environment

Shane Tuohy

Introduction

In 2008, rear end collisions accounted for almost 25% of all injuries sustained in road traffic accidents on Irish roads [RSA Road Collision Factbook 2008]

Effective distance determination can go a long way to reducing injuries

Current Systems

Mercedes Pre-Safe Audi Pre-Sense Plus Toyota Pre-Collision System

All are RADAR systems Expensive Cannot detect humans, animals Susceptible to interference

System Overview

Front facing standard optical camera

Cheap Many uses Simple to install

OpenCV Begun by Intel, currently maintained by

community, under stewardship of Willow Garage

Extensive library of Computer Vision functions

C, C++, Python, Java

No need to continually ‘reinvent the wheel’

System Overview

Capture Image Process (OpenCV) Feedback To User

System Overview

Capture Image Process (OpenCV) Feedback To User

Image Processing Steps

Threshold Image

Warp Perspective

Determine Distance

Thresholding

Remove road surface and highlight objects Sample road surface in front of vehicle Remove pixels ±35 of sampled value Apply binary threshold

Image Processing Steps

Threshold Image

Warp Perspective

Determine Distance

The Problem

Distance in image does not change linearly as vehicle changes position

The Solution

Inverse Perspective Mapping

Inverse Perspective Mapping Geometric transform which allows us

to remove perspective effect

Image Processing Steps

Threshold Image

Warp Perspective

Determine Distance

Distance Determination

All road pixels are zero

Analyze area in front of car

Find first non zero pixels

Translate to distance using scaling factor

Accurate Distance Calibration How can we know this ‘scaling factor’?

Need to calibrate for particular camera setup

Can be done once for given environment and parameters Lay 1m object on road surface Use chessboard pattern of known size

Roughly calculated for project testing

System Overview

Capture Image Process (OpenCV) Feedback To User

Information Overlay

Provide graphical feedback to user

Project Milestones

1. Threshold to remove road surface. Generate transformation matrix

2. Transform image to IPM view

3. Distance determination

4. Graphics overlay

5. Modify algorithm for use on a real time video stream

Conclusion Further work possible

Improve thresholding for different road conditions Improve performance of IPM algorithm Automatic calibration implementation

Paper submitted to ISSC 2010, awaiting review S. Tuohy, D. O Cualain, M. Glavin, E. Jones:“Distance

Determination for an Automobile Environment using Inverse Perspective Mapping in OpenCV”

Successful implementation of proposed algorithm

Demonstration