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
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