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Prepared by:

Precise Object Tracking under Deformation

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

Key subjects of this framework include:

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

Outlines

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.

- 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.

Block Diagram of Object Tracking System

Video Camera

Frame grabber

PC

Image

Acquisition

USB

Camera

USB

Bus

Image

Processing

Output

Target

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

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

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.

Spatial Filters

f(x-1,y-1)

f(x-1,y)

f(x-1,y+1)

f(x,y-1)

f(x,y)

f(x,y+1)

f(x+1,y-1)

f(x+1,y)

f(x+1,y+1)

w(-1,-1)

w(-1,-1)

w(-1,0)

w(-1,0)

w(-1,1)

w(-1,1)

w(0,-1)

w(0,-1)

w(0,0)

w(0,0)

w(0,1)

w(0,1)

w(1,-1)

w(1,-1)

w(1,0)

w(1,0)

w(1,1)

w(1,1)

Pixels of image

The result is the sum of products of the mask coefficients with the corresponding pixels directly under the mask

Mask coefficients

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)

Nonlinear Spatial Filters

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.

Wavelet Transform

Figure 1 The two-dimensional FWT - the analysis filter

Figure 2 Two-scale of two-dimensional decomposition

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

Denoising Proposed Filter

I/p image

Median filter

Coiflet Wavelets

O/p image

Figure 3 Cascaded spatial filter based on median fitter and Coiflet wavelets

Image Similarity Measure

To validate the efficiency of the previous digital filters the following similarity measures have been applied

- 2D Cross Correlation
- Peak Signal-to-Noise Ratio (PSNR)dB

2D Cross Correlation

Table 1. 2D cross correlation similarity measure

Peak Signal-to-Noise Ratio (PSNR)dB

Table 2. PSNR similarity measure

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.

Figure 5 Control point selection

Original image

recovered image

Scaled & rotated image

Figure 6 Recovered by using linear 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:

x' is the perpendicular distance of the beam from the origin and θ is the angle of incidence of the beams.

Original image

Edge detection

Edge linking

Figure7 Canny edge detection and edge linking

Figure 8Radon transform projections along 180 degrees, from -90 to +89

Original image

Rotated image

recovered image

Figure 9 Recovered by using radon transform

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

Deblurring Techniques

Deblurring using Wiener filter

Deblurring using a regularized filter

Deblurring using Lucy-Richardson algorithm

Deblurring using blind deconvolution algorithm

A blurred or degraded image can be approximately described by this equation

Deblurring using the Blind Deconvolution Algorithm

Figure 10Deblurring using the blind deconvolution algorithm

(b) Person detection under

motion deformation

(a) Blurred image

(c)Deblurred image

(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

29

Blurred imagecorrelation with original one

Deblurred image using correct parameterscorrelation

Deblurred image using longer PSFcorrelation

Deblurred image using different anglecorrelation

Figure 12, 2D cross correlation with the deblurring form

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

RGBRepresentation

The RGB color model

mapped to a cube

A Representation of additive color mixing

- Weak points of the RGB color model
- RGB color model is affected bythe change of illumination
- RGB isnon uniform color model

HSV Representations

conical representation

of the HSV

The cylindrical representation of the HSV

HSV color wheel

- Hue, saturation and intensity are often plotted in cylindrical coordinates with hue the angle, saturation the radius, and intensity the axis.

Chrominance is defined as the difference between a color and a reference white at the same luminance.

YCbCr Color Model

The conversion from RGB to YCbCr

The conversion from YCbCr to RGB

Advantage of YCbCr

The main advantages of this model are:

The luminance component (Y) of YCbCr is independent of the 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).

- 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

Original image

sample

RGB

HSV

YCbCr

Figure 13, Comparison of homogeneousobjectextraction

Inhomogeneous Object Extraction

Original image

sample

RGB

HSV

YCbCr

Figure 14, Comparison of inhomogeneousobjectextraction

42

The most basic morphological operations are dilation and erosion

- Dilationadds 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

- 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.

Binary after removing extra pixel

Binary object

Binary object after dilation holes

Binary object after closing

Figure 15, The effect of the morphological operation

Figure 16, Center of gravity, ellipse fitting and bound box of an image

Figure 17 object tracking at different distance

- The relation between distance (D) and no of pixel (N)

Figure 18. The relation between

range (D) and projection size (N)

Where,

a = 30606.621

b=-0.03410108

- 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

Motion Estimation and Prediction based on FIR

Figure 19, FIR model structures

Motion Estimation and Prediction based on FIR

Figure 20, Models output w.r.t system output

Motion Estimation and Prediction based on FIR

Figure 21 Model output w.r.t system output

Motion Estimation and Prediction based on FIR

Figure 22 The capability of the model to predict

the output if the system input is known

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 System

Conclusion and Future Work

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