AUTOMATIC DETECTION OF EMPTY

PARKING SLOTS -OPEN SPACE CAR PARK

Student Name: Honghao ChenSupervisor: Dr Jimmy Li

Co-Supervisor: Dr Sherry Randhawa

Content: Background Introduction

Procedures

Methods

Future Extension

Conclusion

Purpose Circling parking lots Wasting time and effort Providing information via digital displays Open car parks Detecting individual and predetermined

parking slot Video and image processing Real-time and mobile apps

Where to goHow long to takeWhere to go if occupied

Video Mosaicking

Stitch video frames together

A comprehensive view of the scene

A compact representation of the video data

Procedure:

1. Loading a video sequence

2. Matching points between successive frames by

the Corner Matching subsystem

3. Estimating Geometric Transformation block

4. Computing an accurate estimate of the

transformation matrix

5. Overlaying the current video frame onto the

output image

Perspective Transformation To change the ‘perspective’ of the active

content from one state to another

To find a full image

Image Extraction Predetermined region of each parking space Reference setting

Methods

Color Histogram

CPSNR – Color Peak Signal-to-Noise

Ratio

NCD – Normalized Color Difference

Color Histogram

Provide a global description of the

appearance of an image

Produce a level for every pixel value in

the original image

Empty parking slots

Occupied by blue car

Occupied by red car

Procedure:

Describe the levels of the original image

Specify the desired density function

Obtain the transformation function

Apply the inverse transformation

function

CPSNR (Color Peak Signal-to-Noise Ratio)

The ratio between the maximum possible

value (power)

Is expressed in terms of the logarithmic

decibel scale

I: The matrix data of the original image I: The matrix data of the degraded image M, N : The number of rows and columns of input

image R: 255 for an 8-bit unsigned integer data type

Images took from different locations

Comparison

As a quality measurement between the

original and a compressed image

The higher the CPSNR, the better the quality

of the compressed

Normalized Color Difference

Depending on the illumination

Depending on different lighting conditions or

cameras

Allowing for object recognition techniques

based on color

To compensate for variations

Application:

For object recognition on color images

Detect all intensity values from the image while

preserving color values

NormalizedRed = r/sqrt(Red^2 + Green^2 + Blue^2);

NormalizedGreen = g/sqrt(Red^2 +

Green^2 + Blue^2);

NormalizedBlue = b/sqrt(Red^2 + Green^2 + Blue^2);

Choosing a suitable reference

Comparison

Under different lighting conditions

Range of NCDValue

Informationto display

> 17 Occupied

< 8 Available

NCD: 27.3598

Future Extension

Mobile apps for real-time update

-GPS

Extra information

-The nearest slot if possible

Conclusion:

Principle

Procedures

Video Static image Slot reference

Methods (NCD) Display

Results Analysis

Over 17 Occupied

Below 8 Available