1. Student Name: Honghao Chen Supervisor: Dr Jimmy Li Co-Supervisor: Dr Sherry Randhawa

2. Content:  Background Introduction  Procedures  Methods  Future Extension  Conclusion

3. 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

4. Where to go How long to take Where to go if occupied

5. Video Mosaicking  Stitch video frames together  A comprehensive view of the scene  A compact representation of the video data

6. 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

8. Perspective Transformation  To change the ‘perspective’ of the active content from one state to another  To find a full image

9. Image Extraction  Predetermined region of each parking space  Reference setting

10. Methods  Color Histogram  CPSNR – Color Peak Signal-to-Noise Ratio  NCD – Normalized Color Difference

11. Color Histogram  Provide a global description of the appearance of an image  Produce a level for every pixel value in the original image

12. Empty parking slots

13. Occupied by blue car

14. Occupied by red car

15. Procedure:  Describe the levels of the original image  Specify the desired density function  Obtain the transformation function  Apply the inverse transformation function

16. CPSNR (Color Peak Signal-to-Noise Ratio)  The ratio between the maximum possible value (power)  Is expressed in terms of the logarithmic decibel scale

17.  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

18. Images took from different locations

19. Comparison

21.  As a quality measurement between the original and a compressed image  The higher the CPSNR, the better the quality of the compressed

22. 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

23. 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);

24. Choosing a suitable reference

25. Comparison

26. Under different lighting conditions

27. Range of NCD Value Information to display > 17Occupied < 8Available

28. NCD: 27.3598

29. Future Extension  Mobile apps for real-time update -GPS  Extra information -The nearest slot if possible

30. Conclusion:  Principle  Procedures Video  Static image  Slot reference  Methods (NCD)  Display  Results Analysis Over 17  Occupied Below 8  Available