ArticlePDF Available

Moving Objects Detection Based on Frequency Domain

Authors:
  • Imam Kadhim Faculty of Islamic Sciences University

Abstract and Figures

In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.
Content may be subject to copyright.
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

DOI: http://dx.doi.org/10.21123/bsj.2020.17.2.0556 
Moving Objects Detection Based on Frequency Domain
Jalal H. Awad 1* Balsam D. Majeed 2
Received 30/1/2019, Accepted 16//2019, Published 1/6/2020
This work is licensed under a Creative Commons Attribution 4.0 International License. 
Abstract:
In this research a proposed technique is used to enhance the frame difference technique performance
for extracting moving objects in video file. One of the most effective factors in performance dropping is
noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find
a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such
situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet
transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard
deviation) proved that these transformations can stand to overcome such problems in an elegant way.
Key words: Average filter, Fourier transformation, Frame difference,Median filter,Moving objects
detection, Wavelet transformation.
Introduction:
In general, image processing techniques can
be categorized into spatial and frequency domain
techniques. Thus image filtering can be
accomplished in such way. There are two choices to
do image filtering, One of them is in spatial domain
by convolving the image under consideration with
an adequate window (basis image), while filtering
in frequency domain occurs through multiplying the
transformed image with an appropriate low pass
filter (in this research a circle of “ones” with
appropriate radius) (1). Examples of spatial domain
techniques are mean (average), median,
Gaussian….etc. Spatial domain techniques produce
different resulted output signals (images in this
research context). Filtering images in spatial
domain are more computation consumer (2). Hence
it is preferable to try the other techniques
(frequency). Examples of such techniques are
Wavelet, Fourier, Walsh…etc. Spatial domain
techniques depend directly on pixel intensity levels,
whereas frequency domain techniques depend on
frequency coefficients (2). Transformation in spatial
domain is done pixel in one domain to a pixel in
other.
1 Department of Computer, College of Science,
Almustansiriyah University, Baghdad, Iraq.
2 Department of Computer techniques Engineering, Imam
Kadhim Faculty of Islamic Sciences University,
Baghdad, Iraq.
*Corresponding author: jalalhameed@uomustansiriyah.edu.iq
*ORCID ID: https://orcid.org/0000000293136681
But in frequency domain the matter is
different because every pixel in the image when it is
in the spatial domain participates in producing
every value in the frequency domain (1).
The whole operation is sometimes called
projection which is convolution, correlation or
multiplication of the original signal (image) with a
basis function may be sine/cosine, high/low or
Fourier, Wavelet (Haar, daubachies, …etc.) and
then taking the sum of the resulted multiplications,
thus finding the transformation coefficients (1).
Moving object detection is the first step for
successive operations (3). One of the most difficult
tasks is the detection of such objects in videos
which has a great role in segmenting image frame
into static and moving regions (4, 5). Such step can
focus the attention just toward the moving object
which leads to decreasing the computations. It may
also be useful for offline videos indexing,
searching, smart video data mining, community
security, law enforcement (for example car excess
speed) and many military applications (3). There are
multiple approaches for detecting moving objects
like background subtraction, optical flow and frame
difference (5, 6, 7). The last is the one used in this
research.
The aim of this research is to develop a
technique which utilizes the two well-known
Fourier and Wavelet transformations which have so
important features that allow us to convert video
spatial frames information with high degree of noise
and redundancies into less correlated transformation
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

coefficients (frequency domain coefficients)
leading to reducing unnecessary computations as
well as a flexibility in the selection of certain
frequencies which composite the main frame
(image) architecture elements with less noise
elements that lay in some frequency ranges. So the
resulting frame can be treated with frame difference
technique in order to extract the mask of the moving
object easily and clearly.
Materials and Methods:
Orthogonality and orthonormality of the basis
functions:
Transforming an image from spatial to
spectral (frequency) domain requires projecting
(convolving/correlating) what are called basis
images (basis functions) on the input image in order
to convert it into its basis frequency components.
These basis images should have two important
features, namely orthogonality and orthonormality
(4, 8). Orthogonality means that the basis images
are perpendicular to each other which mean that
their projection (inner product) is equal to zero.
This feature is very important when analyzing
signal (image), because the resulted zero of their
inner product means that there is nothing in
common between them (uncorrelated), which lead
to good signal analysis. If they were not so (their
inner product ≠ zero) then the analysis will not
achieve the required right precise analysis. If it to
wonder why this should be, then the answer is that
the transformation aims to analyze any complex
signal (image) into weighted sum of these basis
images. Whereas if these basis images were not
orthogonal then the resulted transformation
coefficients would include useless redundant
information. Whereas orthonomality means that
each basis image magnitude is equal to “1”. These
two features are applicable to the sine and cosine
functions which are used in Fourier transformation
(9).
Sine and cosine functions:
Sine and cosine functions can be clarified
according to fig1 and Fig.2:
Figure 1. The unit circle used for deriving sine
and cosine functions.
Figure 2. The sine and cosine waves for interval
of.
The unit circle of Fig. 1 as well as sine and
cosine amplitude of Fig. 2 are compatible with
orthonormality property of the basis functions (sin
and cos in Fourier context). The “zero” result of the
sine and cosine functions multiplication satisfies the
orthogonality property.
Fourier transformation:
The operation of transforming the image
into the frequency domain is called image analysis
or decomposition which as mentioned before is
done using adequate basis function. The converse
operation is called reconstruction which depends on
the inverse basis function which in turn is almost
the same as or resemble to the one used in the
decomposition step (1). Fourier transformation of
two dimension function can be given with the
following equations (9):






 …..1
 …..2

 







…..3
The inverse of this transformation can be given
according to the following equations:
 




 …..4









…..5
Where is the function to be
transformed,  are its dimensions,  are two
spatial values, and are the frequency
coefficients (9). The are used here because the
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

sine and cosine waves need (360°) in order to
accomplish one cycle as explained in the before
mentioned unit circle. Thus 2πu is called angular
frequency (9).
Fourier transform tends to decompose a
complex signal (can be image) into “zero”
frequency term (DC) and multiple sinusoidal terms
(basis functions), where each sinusoid is a harmonic
of the fundamental basis image (first harmonic)
which has the lowest frequency. The remaining
harmonics are frequency multiples of the
fundamental. (1) In Fourier transformation the value
of u=0 means the slowest frequency (first harmonic)
in the signal under consideration which is wanted to
be analyzed into its principal frequency
components. This frequency is called the zero
frequency. The low frequency components in the
analyzed signal are related to a slow intensity
change in the original signal and vice versa (10).
The reconstruction (inverse Fourier) of the
analyzed signal can be achieved by the sum of these
harmonics which are weighted through the
transformation coefficients. These coefficients tell
how are the magnitude and shift (phase) that each
harmonic (sinusoid) has (1).
In image processing, the filters that can be
used are either low pass filters for blurring or high
pass filters for sharpening. In this research an ideal
low pass filter (ILPF) which is a circle is used as a
mask for specifying which frequencies (harmonics)
to remain and which to be filtered out. The ILPF is
preferable to soft the image or to use its
complement for sharpening (10).
Eq.3 tells how much each harmonic of
specific frequency is presented in signal
(image). Thus according to this equation the shift
for each harmonic occurs through the time (spatial)
domain using increasing variable value ().
Whereas the scaling for such harmonics is done by
applying the same equation with increasing value of
the frequency variable (). Then the summation
operator acts to compose the resulted harmonics
(sine and cosine for example) of multiple scales
(frequency) and various phases (shift) that are
represented in the transformation coefficients in
order to reconstruct the original signal (8). So it can
be briefly concluded that any signal in the spatial
(time) domain is nothing more than a linear
combination (summation) of multiple harmonics
with different amplitudes and frequencies (8, 9).
According to (1) the resulted coefficients
will include real and imaginary parts eq.5. They
both are used to infer how much the magnitude is
and the phase (θ) for each sinusoid (basis function).
…..6

 …..7 (1)
The function F() is periodic and conjugate
symmetric which means that both positive and
negative side of this function are symmetric, thus it
is sufficient to know one value in terms of other
which leads to less computations (9). The Fourier
transformation of a single wave will produce
spectrum with a single positive frequency which
mean that such a wave include just one frequency
(11). As any function can be represented through
its even and odd parts, here the cosine and sine
functions represent the even ad odd parts
respectively.
A simple example of decomposing function 
into its even and odd parts is given in Fig. 3.
Figure 3. the even and odd component of a
function f(t).
Wavelet transformation:
Fourier spectrum gives global information
about the signal but it doesn’t give any information
for that signal within specific period of time. In
contrast to this time domain informs what happen
within any specific time interval without global
information. So it is urgent to find a method that
can encompass the two types, global (frequency)
and localization (time) information (1). The
technique that is suitable for providing such
information is then the Wavelet transformation (12).
Wavelet transformation can be conducted using any
of such basis functions like Daubechies, Morlt,
Meyer, Maxican hat and Haar which is the first and
simplest function used for Wavelet. Of course the
type of such basis function is application dependent.
As noted here these basis functions are the
correspondences of cosine and sine in Fourier
transformation.
Wavelet transformation has two
recognizable functions which are Wavelet and
scaling functions. It also can be recognized as
having good spatial and frequency localization
properties. It decomposes the image into many
multi-resolution components due to the use of low
and high pass filters, these components are one
approximation and three different details which
have different frequency components as LL (low
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

low), LH (low high), HL (high low) and HH (high
high) respectively (1, 12, 13).
Wavelet transformation depends on the
uncertainty principle which states that it is
impossible to get signal that has narrow spatial and
spectral domain at the same time. The law which
enforces a tradeoff between these two is:
Signal duration frequency bandwidth
…..8
(11) According to this, it is necessary to decide
which scaling level (signal duration) is adequate for
an application when using Wavelet transform. In
this research prove that they both provide good
balance between the global information (frequency
bandwidth) and local information (signal duration).
Through the use of Wavelet transformation
it is possible to reduce the noise through removing
the small details which may correspond to noise
without affecting the other details that are related to
edges.
Morphology
It is an image processing which can be used
in order to extract some ROI features like skeleton,
convex hull… etc. or it can be used as a pre/post
processing tool in order to enhance these extracted
regions.
For the sake of such these operations, morphology
uses what is called a structuring element (SE) which
encompasses a set of elements with one of them as a
center. This SE is used in a matter just like
correlation or convolution operations which are
used in spatial domains. Such that the sliding
technique is also used here, by putting the SE center
over the region boundaries and recursively slide
over its pixels till it visits all the region pixels (14).
The two main morphology operations
which other high level operations depend on are
erosion and dilation.
Erosion can be used in order to shrink ROI.
Mathematically it can be given by:
}…9
Where B represents the SE, while A is the ROI.
Dilation can be used in order to enlarge regions.
Mathematically it can be given by:
 …10
where represents reflection of B about its origin.
Opening is a higher level operation which
acts to eliminate the region’s tiny salient and
smoothing its boundary. It can be given as:
 …11
As a result it consists of two consecutive operations
that are Erosion for the region A by SE B followed
by SE dilation of the result with the same SE (14).
Frame difference for moving object detection
Frame difference is an approach for
extracting moving objects in video frames, where
two consecutive video frames are subtracted pixel
wise. Thus in such case if there is any moving
object happened to be exist in any of these frames,
will be extracted as the subtraction result (15).
Proposed algorithm
The algorithm of this work can be described
through the following steps: first two consecutive
frames should be read. Then they need to be filtered
through a spatial or frequency domain filters. To
wipe the static objects from the resulted frames, two
consecutive frames subtraction should be
conducted, considering remaining as moving
objects. Any pixel with intensity less than
predetermined threshold (th) must be removed.
Arbitrarily multiple threshold values have been tried
according to varied elected frequencies (the no. of
elected frequencies increase in direct proportion
with the increasing radius of the circle in case of
fourier transform, and in inverse proportion with the
wavelet levels) to decide which of them is adequate
in each case. The primary moving object mask can
be achieved by converting the resulted image into
binary image. Performing an opening morphology
operation (as described above) with adequate
structuring element (disk shape with radius of 1
pixel) is necessary to get the final moving object
mask. This mask can be used as a reference in order
to circulate the corresponding position of this mask
in the original frame and declare it as moving
object. In the first step deliberately two successive
frames are used in order to ensure accurately no
detail has been ignored. Figure 7 shows such these
two consecutive frames.
Unfortunately in some cases not all video
frames may be noiseless. Therefore in step tow this
research suggests and implements variant spatial
and spectrum (frequency) domain filters for the
sake of removing such this noise. The ordinary
spatial domain filters (mean and median) are used
here for the purpose of performance comparison
with the frequency domain filters. Threshold value
selection in these spatial domain filters for noise
removing depends on pixel intensity values.
However in the case of frequency domain
filters the matter may be somehow different, where
the consequence of the frequency transforms
(Fourier/Wavelet) is frequency coefficients which
requires different threshold selection approach. For
example the result of applying the Fourier transform
on a frame is a matrix of frequency coefficients,
with low (which represents the most important
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

frame information) frequency components
concentrated in the center of this matrix and high
frequency components (frame’s edges and noise) as
it goes away outward this center. Therefore a circle
shape mask as in Fig. 4 with adequate radius is used
to extract (pick) the most important frame
information (low frequencies) ignoring the others
and ultimately applying the threshold on the pixels
intensity values after applying the inverse Fourier
transform.
Another approach is conducted by utilizing
the Wavelet transform to get the frequency
coefficients, then applying an appropriate threshold
on these coefficients in order to preserve the most
approximation sub band coefficients (low
frequencies which represent the most important
frame information) and neglecting some of the other
sub band coefficients (some edges and noise).
Figure 4. A circle mask used to retrieve just the
low Fourier frequencies
Step three is intended to capture any tiny
change in around (which indicate the presence of a
moving object(s)). This can be achieved by
subtracting the resulted two consecutive frames of
step two. Thus the pixels in first frame will be
subtracted from the corresponding pixels in the
second frame, resulting in an image with just
moving object(s) on approximately black
background, an example is shown in Fig. 5.
Figure 5. the resulted difference image after
filtering of the two frames by utilizing low
frequency Fourier coefficients.
This resulted image can be handled with a
predetermined threshold in order to preserve just
pixels with values higher than this threshold, Fig. 6
shows the consequence of this step after converting
to binary form and application of an opening
operation.
Figure 6. moving objects mask
As this mask encompass just the impact of
the moving object(s). It can be superimposed over
the original frame to refer to the location(s) of
this/these moving object(s). It is possible to use
bounding box (es) around such locations to indicate
to moving object(s).
Results and Discussion:
The proposed technique depends on the
difference of two successive frames in order to find
moving objects. An example of such these two
frames is shown in Fig.7.
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

Figure 7. two successive noisy frames with moving objects (cars)
In Fourier transformation, an ILPF (circle)
is used in order to filter out some of unwanted high
frequencies (which may include noise). There is a
positive relation between the circle area and the
ratio of the remaining (unfiltered) frequencies and
hence the cutoff threshold as shown in Table 1.
Table 1 filtering with Fourier transformation using different thresholds and limited number of elected
frequencies. Where hot color map reflects the frame pixels intensity values after picking (selection) of
the appropriate frequency coefficients and applying inverse frequency transform (in case of Fourier
and wavelet), and frame pixels intensity values after applying filter (in case of mean and median
filters).
Filtering
method
Hot color map
Threshold
Fourier
With circle
filter of
radius=2
20
Fourier
With circle
filter of
radius=10
30
Fourier
With circle
filter of
radius=5
55
Fourier
With circle
filter of
radius=10
55
Fourier
With circle
filter of
radius=40
55
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

Fourier
With circle
filter of
radius=10
120
Fourier
With circle
filter of
radius=10
150
Fourier
With circle
filter of
radius=40
150
Fourier
With circle
filter of
radius=60
150
A similar behavior is found through using the
Wavelet transform but with an inverse relationship between the Wavelet decomposition level and the
cutoff frequency (threshold) as shown in Table 2.
Table 2. filtering with Wavelet transformation using different thresholds and limited number of
elected frequencies. The threshold is to determine the elected values.
Filtering
method
Hot color map
Threshold
Mask
Wavelet 5
levels
20
Wavelet 3
levels
30
Wavelet 5
levels
30
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

Wavelet 3
levels
55
Wavelet 5
levels
55
Wavelet 2
levels
55
Wavelet 2
levels
120
Wavelet 1
level
150
Wavelet 3
levels
150
Mean 3x3
55
Mean 5x5
55
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

Mean 3x3
120
Mean 5x5
120
Median 3x3
55
Median 5x5
55
Median 3x3
120
Median 5x5
120
Entropy can be defined as the amount of
information and noise that exist in the signal, or it is
the number of times that the system can be ordered
differently. As long as the entropy is high, the
system instability is also high as well as there will
be lower system harmony. So in image for instance
as the pixels values are close to each other, the
entropy scalar value will be lesser and vice versa.
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

Image entropy can be calculated using the following
formula: (14)
 

 …12
Where is the Kth intensity value, is the
probability of occurrence of this intensity level.
High standard deviation values means that
there is a high dispersion between these values.
According to image concepts, taking the low
frequencies means that it is intended to take the
uniform (close intensity values) regions. Therefore
the consecutive frames subtraction results small
values due to the subtraction of these corresponding
uniform regions in both under consideration frames.
So the selected threshold must be small in order to
be suitable for such these before mentioned resulted
subtraction values. But in case of taking low and
high frequencies, which means that there will be
uniform regions in addition to abrupt changes
causing the subtracted values to hold both small and
high values which affect the threshold selection
toward higher values. Standard deviation can be
given by (14):


 …13
Where m is the mean intensity value.
All the used filter types tend to decrease
differences between neighbor pixels values. This is
to diminish noise just like blurring does. This may
sound to decrease the Std, but in fact this at the
same time create groups (blocks of pixels with each
of such groups having pixels sharing the same
value). Thus a lot of the original frame pixels values
to disappear causing increasing Std value. This Std
value tend to increase whenever these regions
(groups) increase. For example vector A=[1 2 3 4]
has a Std of 1.2910 which is lower than the Std of
vector B=[1 1 4 4] that has a Std of 1.7321. this
behavior tend to converse when these regions grow
up more and more, because in this case a lot of
pixels will have the same value in each region
which in turn means low Std. high Stds give better
flexibility in the selection of a threshold from a
wider range of values that the low Stds which limits
(narrows) this range. Figure 8 shows the normal
distribution of standard deviation.
Figure 8. Std distribution shape
As the circle shrink for Fourier and the
decomposition level increases for Wavelet, the
elected frequencies decrease, which mean that the
selected frequencies will be the low frequencies
where there is no abrupt changes in the values. So
the resulted frames won’t include a lot of edges as
well as noise. Then the difference of the two
consecutive filtered frames will include growing
unified values regions leading to entropy decreasing
as well as the thresholding value and vice versa as
shown in Fig 9. Then the whole matter is a tradeoff
between all of these things.
(a)
(b)
(c)
Figure 9. Entropy & Std with increasing
encompassed frequencies for (a) Fourier
transform, (b) Wavelet transform, (c) Average
filter.
0
2
4
6
8
10
12
14
Noisy
frame
fourier
radius=60
fourier
radius=40
fourier
radius=30
fourier
radius=10
fourier
radius=5
fourier
radius=2
Entropy Std
0
2
4
6
8
10
12
Noisy framewavelet
level=1
wavelet
level=2
wavelet
level=3
wavelet
level=4
wavelet
level=5
Entropy Std
0
2
4
6
8
10
12
Noisy frameaverage 3x3average 5x5Average 7x7
Entropy Std
Open Access Baghdad Science Journal P-ISSN: 2078-8665
2020, 17(2):556-566 E-ISSN: 2411-7986

Acknowledgement:
It is our pleasure to express our
appreciation and thanks for Computer Science
Department/ College of Science/ University of
Mustansiriyah/ Baghdad/Iraq and Department of
Computer techniques Engineering/Imam Kadhim
Faculty of University Islamic Sciences for the
valuable assistance and encouragement to
accomplish this research
Authors' declaration:
- Conflicts of Interest: None.
- We hereby confirm that all the Figures and
Tables in the manuscript are mine ours. Besides,
the Figures and images, which are not mine ours,
have been given the permission for re-
publication attached with the manuscript.
- Ethical Clearance: The project was approved by
the local ethical committee in Almustansiriyah
University.
References:
1. Umbaugh S.Digital Image Processing and
Analysis.Book. CRC press, 2011;2nd ed.
2. Naik A, Barot N, Brahmbhatt R, Dahiya V.
Comparison Between Spatial and Frequency Domain
Methods.IJERMT.2015; 4(12):45-50.
3. Dedeo glu, Y. Moving Object Detection, Tracking
and Classification for Smart Video Surveillance. MSc
Thesis, Bilkent University, 2004.
4. Dong L, Ganesh S. Minimum Delay Moving Object
Detection, IEEE CVPR. 2017; 4250-4259.
5. Pranali A, Ajay A. Review on Automatic Fast
Moving Object Detection in Video of Surveillance
System. IJSRST. 2017; 3(3):545-549.
6. Pavankumar K, Satone M. Moving Object Detection
Survey using Background Detection Methods. IRJET.
2017; 4(5): 1836-1838.
7. Shilpa, Prathap L,Sunitha R.A Survey on Moving
Object Detection and Tracking Techniques. IJECS.
2016;5(5): 16376-16382.
8. Ayush B, Yonina E. Sampling and Super-resolution
of Sparse Signals Beyond the Fourier Domain.
JLCF.2018; 67(6): 1508-1521.
9. Svoboda T, Kybic J, Hlavac V. Image Processing
Analysis and Machine Vision a Matlab Companion.
Book, Thomson/West, 2008.
10. Shaikh S, Choudhry A, Wadhwani R.Analysis of
Digital Image Filters in Frequency Domain. IJCA.
2016; 140(6): 12-19.
11. Sonka M, Halvak V,Boyle R.Image Processing,
Analysis, and Machine Vision. Book. Thomson/West
2008; 3rd ed.
12. Dipalee G, Siddhartha C. Discrete Wavelet
Transform for Image Processing. IJETAE. 2015;
4(3): 598-602.
13. Narjes K, Rania F, Mohamed B. A Fast Selective
Image Encryption Using Discrete Wavelet Transform
and Chaotic Systems Synchronization. ITC. 2016;
45(3): 235-242.
14. Gonzalez R, Woods R. Digital Image Processing.
Book, Pearson Education. Inc., 2008; 3rd ed.
15. Sharma R, Gupta S. A Survey on Moving Object
Detection and Tracking Based On Background
Subtraction. OJIDDS. 2018; 2018(1): 55-62.
 






 
  


., 
... The first step for each video surveillance system is the moving object detection [1]. Intuitively object detection is the preliminary stage for the successive tracking operation [2]- [4]. Employing human beings is inefficient way for monitoring places because of their limited capabilities and high cost demands as compared with the automated surveillance systems [5]. ...
Article
Full-text available
Moving objects detection is a vital field of study in various applications. Many of such applications may have to capture and process a lot of data, then such these data need to be reduced as much as possible in order to have a reasonable and suitable system for achieving the desired aims efficiently. The proposed algorithm utilizes singular value decomposition (SVD) and Bayer pattern filter for their good properties in producing very representative reduced data. This data is then handled by frame difference objects detection, which in turn is an approach that doesn’t need to handle much data. The camera shaking which can be caused by a windy weather in the case of the outdoor static camera may introduce a frame difference with imprecise moving objects detection, hence frames compensation is conducted utilizing a transformation based on speed up robust feature transform (SURF) detected key points.</span
Article
Full-text available
Recovering a sparse signal from its low-pass projections in the Fourier domain is a problem of broad interest in science and engineering and is commonly referred to as super- resolution. In many cases, however, Fourier domain may not be the natural choice. For example, in holography, low-pass projections of sparse signals are obtained in the Fresnel domain. Similarly, time-varying system identification relies on low-pass projections on the space of linear frequency modulated signals. In this paper, we study the recovery of sparse signals from low- pass projections in the Special Affine Fourier Transform domain (SAFT). The SAFT parametrically generalizes a number of well known unitary transformations that are used in signal processing and optics. In analogy to the Shannon's sampling framework, we specify sampling theorems for recovery of sparse signals considering three specific cases: (1) sampling with arbitrary, bandlimited kernels, (2) sampling with smooth, time-limited kernels and, (3) recovery from Gabor transform measurements linked with the SAFT domain. Our work offers a unifying perspective on the sparse sampling problem which is compatible with the Fourier, Fresnel and Fractional Fourier domain based results. In deriving our results, we introduce the SAFT series (analogous to the Fourier series) and the short time SAFT, and study convolution theorems that establish a convolution multiplication property in the SAFT domain.
Article
Full-text available
In this paper, a new and robust image encryption scheme, based on chaotic system synchronization and Discrete Wavelet Transform (DWT), is proposed. First, a discrete wavelet transform is used to decompose the image and to decorrelate its pixels into approximation and detail components. Then, a selective image encryption, based on chaotic algorithm, is applied to encrypt only a subset of data. To recover the original image, the proposed synchronization scheme is based on the use of aggregation techniques and Borne and Gentina practical criterion for stability study associated to the Benrejeb arrow form matrix for system description. Experimental results using hyperchaotic Hénon map demonstrate that the proposed cipher own has a good resistance against brute force and statistical attacks.
Article
MOVING OBJECT DETECTION, TRACKING ANDCLASSIFICATION FOR SMART VIDEOSURVEILLANCEM.S. in Computer EngineeringSupervisor: Assist. Prof. Dr. Ugur GudukbayAugust, 2004Video surveillance has long been in use to monitor security sensitive areas suchas banks, department stores, highways, crowded public places and borders. Theadvance in computing power, availability of large-capacity storage devices andhigh speed network infrastructure paved the way for cheaper, multi sensor video...
Article
Digital image processing methods were applied to improve the practicability of cephalometric analysis. The individual X-ray film was digitized by the aid of a high resolution microscope-photometer. Digital processing was done using a VAX 8600 computer system. An improvement of the image quality was achieved by means of various digital enhancement and filtering techniques.
  • A Naik
  • N Barot
  • R Brahmbhatt
  • V Dahiya
Naik A, Barot N, Brahmbhatt R, Dahiya V. Comparison Between Spatial and Frequency Domain Methods.IJERMT.2015; 4(12):45-50.
Minimum Delay Moving Object Detection
  • L Dong
  • S Ganesh
Dong L, Ganesh S. Minimum Delay Moving Object Detection, IEEE CVPR. 2017; 4250-4259.
Review on Automatic Fast Moving Object Detection in Video of Surveillance System
  • A Pranali
  • A Ajay
Pranali A, Ajay A. Review on Automatic Fast Moving Object Detection in Video of Surveillance System. IJSRST. 2017; 3(3):545-549.