Precise Object Tracking
under Deformation
Prepared by:
Eng. Mohamed Hassan, EAEA
Supervised by:
Prof. Dr. Hussien Konber, Al Azhar University
Prof. Dr. Mohamoud Ashour, EAEA
Dr. Ashraf Aboshosha, EAEA
Submitted to:
Communication & Electronics Dept.,
Al Azhar University
1
Outlines
Key subjects of this framework include:
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2
Motivation
Visual tracking applications
Block diagram of object tracking system
Image deformation types
Object extraction
Morphological operations
Geometrical Modeling and pose estimation
Conclusion and Future Work
Motivation
The main objectives of this research work are to:

Overcome the imprecision in object tracking caused
by different deformation sources such as noise,
change of illumination, blurring, scaling and rotation.

Developing a three dimensional (3D) geometrical
model to determine the current pose of an object and
predict its future location based on FIR model

Presenting a robust ranging technique to track a
visual target instead of the traditional expensive
ranging sensors.
3
Visual Tracking Applications
 The precise object tracking is an essential issue in
several applications such as:
 Robot vision
 Automated surveillance (civil and military)
 Medical applications
 Satellite and space systems
 Traffic systems
 Security etc.
4
Block Diagram of Object
Tracking System
Video
Camera
Frame
grabber
PC
USB
Camera
USB
Bus
Image
Acquisition
Image
Processing
Output
Target
5
Image Deformation Types
 Noise.
 Scaling &Rotation.
 Blurring
 Change of illumination.
6
Image Deformation: Noise
Definition: is considered to be any measurement
that is not part of the phenomena of interest. Images
are affected by different types of noise:
 Gaussian noise
 Salt and Pepper noise
 Poisson Noise
 Speckle Noise
7
Image De-noising Techniques
The following digital filters have been employed
for denoising
 Linear filter (Average filter, Gaussian filter and unsharp
filter)

Non linear filter (Median filter and Adaptive filter)
 Coiflet Wavelets
 Proposed filter
8
Spatial Filters
 Spatial filtering term is the filtering operations that are
performed directly on the pixels of an image.
 The process consists simply of moving the filter mask from
point to point in an image.
 At each point (x,y) the response of the filter at that point is
calculated using a predefined relationship.
9
Linear Spatial Filters
Pixels of image
f(x,y)
w(0,1)
The result is the sum of
products of the mask
coefficients with the
corresponding pixels directly
under the mask
f(x,y+1)
w(1,0)
w(1,1)
Mask coefficients
w(-1,-1) w(-1,0) w(-1,1)
f(x-1,y-1) f(x-1,y) f(x-1,y+1)
w(0,-1)
w(0,0)
w(1,-1)
f(x,y-1)
f(x+1,y-1) f(x+1,y) f(x+1,y+1)
10
w(-1,-1) w(-1,0) w(-1,1)
w(0,-1)
w(0,0)
w(0,1)
w(1,-1)
w(1,0)
w(1,1)
Nonlinear Spatial Filters
 Nonlinear spatial filters also operate on neighborhoods,
and the mechanics of sliding a mask past an image are the
same as was just outlined.
 The filtering operation is based conditionally on the values
of the pixels in the neighborhood under consideration.
 Order-statistics filters are nonlinear spatial filters whose
response is based on ordering (ranking)
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Wavelet Transform
 The Wavelet transform is a multiresolution analysis tool
which decomposes a signal into different frequency sub
bands.
 Wavelet transform, due to its excellent localization, has
rapidly become an indispensable signal and image
processing tool for a variety of applications.
 Wavelet denoising attempts to remove the noise present in
the signal while preserving the signal characteristics,
regardless of its frequency content.
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Wavelet Transform
Figure 1 The two-dimensional FWT - the analysis filter
13
Figure 2 Two-scale of two-dimensional decomposition
Denoising Proposed Filter
 The proposed filter is a cascaded spatial filter based on median
fitter and Coiflet wavelets. Its edge-preserving nature makes it
useful in cases where edge blurring is undesirable. It is very
useful in real object tracking. This filter is the best one for
removing all types of noise
I/p image
Median filter
Coiflet Wavelets
O/p image
Figure 3 Cascaded spatial filter based on median fitter and Coiflet wavelets
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Image Similarity Measure
To validate the efficiency of the previous digital filters the
following similarity measures have been applied
2D Cross Correlation
i n

 [ x
i
 m x  *  y i  m y ]
i 1
i n
 x
i
mx
i 1
i n
  yi
2
my

2
i 1
 Peak Signal-to-Noise Ratio (PSNR)dB
 M ax I 
PS N R  20  log 10 

M
S
E


15
M SE 
1
mn
m 1

i 0
n 1
 P I  i , j   k (i , j ) P
2
j 0
2D Cross Correlation
Unsharp
filter
Average
filter
Gaussian Median
filter
filter
Adaptive
filter
Proposed
filter
Salt and
paper
noise
0.9234
0.9890
0.6983
0.9809
0.7804
0.9984
Gaussian
noise
0.5651
0.9861
0.9446
0.9701
0.9701
0.9876
Poisson
noise
0.8270
0.9920
0.9900
0.9910
0.9913
0.9961
Speckle
noise
0.6349
0.9879
0.7737
0.8341
0.8547
0.9871
Table 1. 2D cross correlation similarity measure
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Peak Signal-to-Noise Ratio
(PSNR)dB
Salt and
paper
noise
Unsharp Average
filter
filter
Gaussian
filter
Median
filter
Adaptiv
e filter
Proposed
filter
18.59
25.49
36.00
22.97
49.48
26.16
23.80
26.42
26.79
32.80
Gaussian 9.94
noise
27.37
Poisson
noise
14.74
28.71
30.21
31.92
32.80
43.16
Speckle
noise
10.86
26.73
25.38
26.71
27.59
37.67
Table 2. PSNR similarity measure
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Scaling & Rotation
Definition: Scaling & rotation is affine Transformation
where Straight lines remain straight, and parallel lines
remain parallel.
Scaling and Rotation: The linear transformation and
radon transformation have been used to recover an image
from a rotated and scaled origin.
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Scaling & Rotation
Original image
Scaled image
19
Scaled &rotated image
Figure 4 Rotated and scaled image
Linear Transformation
Figure 5 Control point selection
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Linear Transformation
Original image
Scaled & rotated image
21
recovered image
Figure 6 Recovered by using linear transformation
Radon Transformation
Radon transform: This transform is able to transform two
dimensional images with lines into a domain of possible line
parameters, where each line in the image will give a peak positioned
at the corresponding line parameters.
Projections can be computed along any angle θ, by use general
equation of the Radon transformation:


R   x      f  x , y   x cos   y sin   x   dy dy
 
w here ,   .  is the delta function
x' is the perpendicular distance of the beam from the origin and θ is
the angle of incidence of the beams.
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Radon Transformation
Original image
Edge detection
23
Edge linking
Figure7 Canny edge detection and edge linking
Radon Transformation
24
Figure 8 Radon transform projections along 180 degrees, from
-90 to +89
Radon Transformation
Original image
Rotated image
25
recovered image
Figure 9 Recovered by using radon transform
Blurring
Blurring: degradation of an image can be caused by
motion
There are two types of blurring
Known blurring: the length and the angle of
blurring are known
Unknown blurring: the length and the angle of
blurring are unknown
26
Deblurring Techniques
A blurred or degraded image can be approximately described
by this equation
g=




27
H  f 
+ n
Deblurring using Wiener filter
Deblurring using a regularized filter
Deblurring using Lucy-Richardson algorithm
Deblurring using blind deconvolution algorithm
Deblurring using the Blind
Deconvolution Algorithm
Figure 10 Deblurring using the blind deconvolution algorithm
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Deblurring Techniques
(a) Blurred image
(c)Deblurred image
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(b) Person detection under
motion deformation
(d) Person detection in
deblurred image
Figure 11, Capability of object tracking under blurring (a, b)
with known blur function and after deblurring (c, d
Deblurring Techniques
Blurred image correlation with original one
30
Deblurred image using correct parameters correlation
Deblurring Techniques
Deblurred image using longer PSF correlation
Deblurred image using different angle correlation
Figure 12, 2D cross correlation with the deblurring form
31
Deblurring Techniques
Correlation Condition
32

blurred image with the original one
0.0614
deblurred image with the original one
using correct parameters
0.3523
deblurred image with the original one
using longer PSF
0.0558
deblurred image with the original one
using different angle
0.1231
Table 3, 2D cross correlation with the deblurring form
Change of Illumination
Change of illumination
Color model deformation may happen due to the
change in illumination
Proposed solution
Selecting an appropriate color model (RGB, HSV or
ycbcr) to overcome the deformation problem
33
RGB Representation
A Representation of additive color mixing
The RGB color model
mapped to a cube
 Weak points of the RGB color model
 RGB color model is affected by the change of illumination
 RGB is non uniform color model
34
HSV Representations
 Hue, saturation and intensity are often plotted in cylindrical
coordinates with hue the angle, saturation the radius, and
intensity the axis.
conical representation
of the HSV
35
The cylindrical
representation of the HSV
HSV color wheel
YCbCr Color Model
 Chrominance is defined as the difference between a
color and a reference white at the same luminance.
The conversion from RGB to YCbCr
Y

C
 b
C r
0.504
0.098   R  16 
  0.257
 
  

  0.148  0.291 0.439
G  128
 
  

  0.439  0.368  0.071   B  128 
The conversion from YCbCr to RGB
R

G

 B
36
1.598  Y  16 
 1.164 0.000
 


 1.164  0.329  0.813 C b  128
 


 1.164 2.017
0.000  C r  128 
Advantage of YCbCr
The main advantages of this model are:
 The luminance component (Y) of YCbCr is independent of the





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color
The skin color cluster is more compact in YCbCr than in other
color space
YCbCr has the smallest overlap between skin and non-skin data
in under various illumination conditions.
YCbCr is broadly utilized in video compression standards
YCbCr is a family of color spaces used in video systems.
YCbCr is one of two primary color spaces used to represent
digital component video (the other is RGB).
Object Extraction
 To track a visual target we have to relay on a
segmentation technique such as:
Thresholding
Clustering
Region growing
Edge-based
Physical model-based
Frame Subtraction
Fast block matching
Throughout this framework a color table thresholding
segmentation technique has been applied to extract
the visual target
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Homogeneous Object Extraction
Original image
Tracked object
sample
39
Homogeneous Object Extraction
Original image
sample
RGB
40
HSV
YCbCr
Figure 13, Comparison of homogeneous object extraction
Inhomogeneous Object Extraction
Original image
Tracked object
sample
41
Inhomogeneous Object Extraction
Original image
sample
RGB
HSV
YCbCr
Figure 14, Comparison of inhomogeneous object extraction
42
Morphological operations
The most basic morphological operations are dilation and erosion
Dilation adds pixels to the boundaries of objects in an image.
Expand/enlarge objects in the image
Fill gaps or bays of insufficient width
Fill small holes of sufficiently small size
Connects objects separated by a distance less than the
size of the window
Erosion removes pixels on object boundaries.
to erode away the boundaries of regions of foreground
pixels (i.e. white pixels, typically).
 Thus areas of foreground pixels shrink in size, and
holes within those areas become larger
43
Morphological operations
 Opening and Closing are morphological operations which are
based on dilation and erosion.
 Opening smoothes the contours of objects, breaks narrow
isthmuses and eliminates thin protrusions.
 Closing also produces the smoothing of sections of contours but
fuses narrow breaks, fills gaps in the contour and eliminates small
holes.
 Opening is basically erosion followed by dilation while closing is
dilation followed by erosion.
44
Morphological operations
Binary object
Binary object after
dilation holes
45
Binary after
removing extra pixel
Binary object after closing
Figure 15, The effect of the morphological operation
Morphological operations
46
Figure 16, Center of gravity, ellipse fitting and bound box of an image
Geometrical Modeling
47
Figure 17 object tracking at different distance
Geometrical Modeling
The relation between distance (D) and no of pixel (N)
N  ae
48
bD
Where,
a = 30606.621
b=-0.03410108
Figure 18. The relation between
range (D) and projection size (N)
Geometrical Modeling
The relation between the range and location of the
object in 3D domain
Figure 19. The relation between the range
and location of the object in 3D domain
49
Motion Estimation and
Prediction based on FIR
Figure 19, FIR model structures
y t

n


i 1
50
au  t  i
  e t

 a u t   e t
T

Motion Estimation and
Prediction based on FIR
Figure 20, Models output w.r.t system output
51
Motion Estimation and
Prediction based on FIR
Figure 21 Model output w.r.t system output
52
Motion Estimation and
Prediction based on FIR
Figure 22 The capability of the model to predict
the output if the system input is known
53
Conclusion and Future Work
Throughout this framework the following academic tasks
have been achieved
 Developing a novel Universal filter for image denoising
 Selecting qualitative radon transformation for correction of the
rotation
 Intensive comparative study for dealing with kwon/unknown
bulrring
 Employing a color table thresholding segmentation technique
on YCbCr to extract the visual target
 3D Geometrical modeling for estimation and prediction of target
pose
 As a future work, we are going to implement the applied
algorithm on an embedded system to develop a visual RADAR
54
System
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