Student Name: Honghao Chen Supervisor: 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 go How long to take Where 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 NCD Value Information to display > 17Occupied < 8Available

NCD:

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