DETECTION AND DETERMINATION COORDINATES OF MOVING OBJECTS FROM A VIDEO
Mamaraufov Odil Abdikhamitovich,
scientific researcher, the Department of Software of IT,
Tashkent University of IT,
E-mail: odil.mamaraufov@gmail.com
DETECTION AND DETERMINATION COORDINATES
OF MOVING OBJECTS FROM A VIDEO
Abstract: This paper is devoted to the creation of an efficient algorithm for determining the motion detection and isolation of an object based on background subtraction using dynamic threshold and morphology in the process. Background subtraction algorithm may also be used to detect
multiple objects. Video images of human to identify methods to distinguish moving objects, the
image of the reference image based on moving images through the search. Main components of the
paper based on the analyses of stage of few hundred dynamic objects image sector, the separation
and identification algorithms and software development. Algorithms developed can also be used for
other applications (real-time classification of objects, etc.).
Keywords: Video surveillance, motion detection, background subtraction, background.
Introduction. This time the relevant tasks are the
collection, analуsis and processing of information on
road safetу, safetу control, traffic on city streets and
highways, road accidents and their study. Also relevant
is the problem of determining the speed of traffic on motorways, registration of motor vehicles at intersections,
posts and vehicle registration, car traffic and frequent
road accidents. So important is the creation and implementation of video surveillance systems installed in roads
and intersections. For the video surveillance system is an
actual resolution of contradictions between the quality
of the generated image and hardware of existing channels of communication and data storage. In spite of the
high capacity, the modern hard disks are not sufficient for
storing large amounts of information for a long time, as it
should be according to the specifications. Traditionally
this contradiction is resolved by video compression with
a noticeable decrease in their quality and loss of information. To improve the efficiency of video surveillance
systems need to develop methods for video data compression without loss of information about the object
of interest for the long term storage and transfer in real
time, high-quality images via communication channels
with limited bandwidth [3]. Video moving object leads
to the appearance of two phases-phase of adaptation to
the current camera angle shooting and maintenance of
objects of interest. The fixed camera shot scene with little
changing background (relative sequence) with moving
objects is of great practical use in observation systems
(maintenance of vehicles, people), security systems, etc.
Existing Work. Currently, there are many methods
of detecting motion, or in another way, methods of subtracting the background. In addition to methods of subtracting the background, methods for preprocessing and
post-processing of data were implemented: the Gaussian
filter, binarization, and the median filter. In the literature,
there is a comparative analysis of video motion detection
methods based on execution time, consumed resources
and calculation of metrics: precision, recall, F-measure.
None of the background modeling algorithms can cope
with all possible problems. A good background subtraction algorithm should be resistant to changes in lighting
and avoidance of non-stationary background objects
such as swinging leaves, grass, rain, snow and shadows
from moving objects should be avoided. And the background model must respond quickly to changes: such as
starting and stopping vehicles or any other starting point.
In the practice of disclosure and investigation of
crimes, materials obtained by television monitoring systems that allow recording certain elements of the mechanism of committing a crime and criminals by means of
video recording are quite often used. During the analysis
of video images, forensic information can be obtained
that helps to establish the spatial characteristics of the
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Section 11. Technical science
imprinted objects, their group identity and to carry out
identification [4].
Existing gabitoscopic techniques of forensic identification of a person on the grounds of appearance [5],
imprinted on a video images are based on portrait identification using static images [6]. In some cases, these
methods are powerless, for example, when the person of
the offender is not visible, fixed in an unsuccessful foreshortening, hidden by a mask or dressed up.
The materials of the video recording contain a considerable amount of information on the volume and
variety related to the functional elements of the appearance of a person (gait, facial expressions, articulation,
gestures, etc.) [4]. These elements can be fixed and perceived for research only in dynamics as static illustrations
of a person’s appearance do not convey these properties.
Dynamic properties of changes in the appearance of a
person are very informative and are characterized by individuality, dynamic stability, selective variability, which
allows them to be used to solve forensic tasks.
Recently, work is underway in our country and
abroad to create new biometric technologies and security systems that measure various parameters and
characteristics of humans [5]. These include the individual anatomical characteristics of a person, except
for the face and fingerprints (the eye fundus, the iris
of the eye, the shape of the palm of the hand, etc.), and
its physiological and behavioral properties (voice, signature, gait, etc.). Such systems are created to restrict
access to information, prevent intruders from entering
protected areas and premises, but they can also be used
in forensic science. The modern capabilities of biometric technologies already provide the necessary requirements for reliability of identification, easy for use and
low cost of equipment.
Methods of linear filtering images. The filtering
methods when evaluating the real signal at some point
in the frame to take into account a lot of (neighborhood)
of neighboring pixels, using a certain similarity of the signal at these points. The concept of neighborhood is fairly
conventional. The neighborhood can be formed only
on frame closest neighbors, but may be a neighborhood
containing enough and strong enough distant points of
the frame. In this case, the degree of influence (weight) of
the far and near points on the decisions taken by the filter
at a given point of the frame, will be completely different.
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Thus, the filtering based on the ideology of the rational
use of the data as a working point, and from its vicinity.
For solving the problems of filtration using probabilistic models and image noise and statistical optimality
criteria apply. This is due to the random nature of the
interference and the desire to obtain the minimum difference in average processing result from the ideal signal.
The variety of methods and filtering algorithms associated with a wide variety of mathematical models and
noise signals, as well as various optimality criteria.
There is a whole class of methods attenuation noisy
images, as well as able to perform other operations (blur,
border selection) based on the original image of the linear filtering. Linear filtering involves the use of a pixel
raster spatially invariant transformation that would emphasize the necessary elements of the image and to avoid
the influence of other, less important.
The filter is based on linear convolution operation,
which is written for the discrete case as in formula (1)
q [m ,n ] = f m ,n ]g [ m ,n  (1)
where q – resulting raster obtained after convolution, m,
n – coordinates x and y, which is performed in the convolution, f, g – baseline rasters. As raster f taken convolution
kernel, which is a matrix of small dimension (usually no
more than 5 × 5 elements), g – the original image, which
should be filtered.
In more detail the operation of convolution is painted in the formula (2)
u /2
q [m ,n ] = ∑
v /2
∑ f [ j ,k ] ⋅ g [m − j ,n − k ], (2)
j =−u /2k =− v /2
where j, k – meters horizontally and vertically in the calculation of convolution, u, v – linear convolution kernel
sizes (width, height), the remaining symbols correspond
to those of the formula (1).
One of the special case of application of the linear filter to the video images is the implementation for the purpose of smoothing convolution. Anti-aliasing smoothes
jumps brightness of the image and allows you to remove
unwanted noise.
Most often used for smoothing kernel convolution
3 × 3, since in most cases they can achieve the desired effect, the price is relatively high, but still acceptable effort.
Kernels fold dimension of 5 × 5.7 × 7, etc. They used very
rarely because of too much labor. For example, the use of
5 × 5 convolution with the kernel, in comparison with
the case of giving a 3 × 3 (5/3) * (5/3) = 25/9 ≈ 2.78
DETECTION AND DETERMINATION COORDINATES OF MOVING OBJECTS FROM A VIDEO
times more processing operations on a single image pixel.
Odds before the cores are selected so that the conversion
did not cause the displacement of the original brightness
of the image.
As you can see, the core of the convolution of even
minimal dimension (3 × 3) will significantly distort the
image, because the color of any specific pixel will affect
its neighbors. Moreover, the larger dimension of the convolution kernel, the greater will be the impact of this. In
addition, the smaller the ratio of the central matrix for the
convolution kernel (the central coefficient corresponds
to the pixel being processed), the greater the distortion.
Distortion during this processing method is expressed as
the lubrication of small parts, blurring edges, smoothing
contrast transitions.
Second we are interested in how to use linear filtering
to the processing of video images – the convolution with
the kernel to emphasize the edges (aka Edge Detect).
Such convolution kernel allows highlight the contours
of objects and to suppress other elements of the image.
Among the convolution kernels for this purpose, there
are several basic is: Sobel Edge Detector, Gauss Edge Detector, Prewitt Edge Detector, Second-Derivatives filter.
Matrix for them as follows (4).
Median filter, unlike the smoothing filter, realizes a
non-linear noise reduction process. As for the impulse
noise, then, for example, a median filter with a window of
3x3 is completely suppresses single uniform background
emissions as well as a group of two, three or four pulsed
emission. In general, for suppressing impulse noise band
the window size should be at least twice the size of the
group of interference. Along with the important advantage of the median filter is the fact that it is significantly
less blurs the contours of objects in the image, this approach has a major drawback associated with the fact that
the calculation of the median requires additional computational cost. After all, despite the fact that the complexity of calculating the median of the array element in
the middle – there is order O(n), is necessary to resort
to additional tricks to practice really achieve linear complexity. But worst case reaches the difficulty still O(n2),
especially if the number of elements in the array is small
(in this case, there are only 9).
Brightness and Contrast Adjustments. We looked at
some ways to remove noise from the original image. However, the stage of pre-processing of video on it usually does
not end there. Removing noise to minimize the number
of false positives in the difference motion detector from
interfering with reception and transmission of images, but
the images containing the same scene, then can still vary
significantly. The reason for this difference is the change
in the level of illumination for registration of different
frames. Changing the lighting level it may be caused by
turning on or off the artificial lighting or weather conditions change, if the shooting is done outdoors. Usually at
this point element wise difference between two adjacent
frames reach very high values, which leads to false alarm
detector, which detects the movement of the whole space
lighting changes. To avoid such false alarms, at the stage
of pre-treatment is necessary to resort to an adjustment
of the brightness level of video [4, 8].
In calculating the brightness of the pixels of the current frame will be based on the pixels of the previous
frame:
I2(x, y) = A(x, y)·I1(x, y) + B(x, y),
(3)
where I1(x, y) – the brightness value of the pixel of the
previous frame, I2(x, y) – the brightness value of the pixel
of the current frame, a(x, y), b(x, y) – linear transform
coefficients.
In general, a and b are the sets of data containing different values for each pixel. In practice, this requirement
is usually simpler and replaced by an algorithm window
area processing several tens of pixels within which the
calculated values of the coefficients a and b, followed by
an adjustment of these pixel values falling within a window in the current frame. Then, the window is moved to
a new location, thus avoiding the entire frame.
It only remains to calculate the coefficients a and b.
In [8] proposed two methods, one of which is based on
the multiplication of luminance values of pixels of the
current frame by a factor that converts the current frame
to the same average luminance as in the previous frame:
a ( x , y ) = I 2 / I 1 (4)
where I 1 and I 2 – the average brightness values in the
previous and current frames, respectively. At this shear
rate is not used:
b(x, y) = 0.
(5)
The second way to agree on neighboring frames on
the average brightness value and variance:
a (x , y ) = σ 2 / σ1,
(6)
b ( x , y ) = I 2 − I 1σ 2 / σ 1
(
)
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Section 11. Technical science
where σ1 and σ2 – mean square deviation of brightness
in the previous and current frames, respectively, the remaining symbols are the same as in (4) and (5). Note
that the processing of a sliding window averages and
standard deviations of the value calculated by the inner
pixels of the window.
Histogram bias is eliminated by the linear contrast,
the idea of which is to convert the intensity of each pixel
of the image with respect to the maximum and minimum
values in the histogram for the current frame:
u ′[m ,n ] = (u [m ,n ] − c ) ⋅ (b − c ) / (d − c ) + a ,(7)
where u[m, n] – pixel of the original image with the coordinates (m, n), u’ [m, n] – pixel of the output image
with coordinates (m, n), a – the lowest possible intensity
value, b – the maximum possible value intensity, с – the
minimum intensity value among pixels of the frame, d –
the highest possible intensity value among the pixels of
the frame.
Thus, all the intensity values that fall between the
percentiles will be scaled and the other extreme values
of a gain or b:
a ,� if
� u [m ,n ] ≤ plow % � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �
 p
u ′[m ,n ] =  u [m ,n ],� if
� plow % < u [m ,n ] < phigh % (8)
 b ,� if
 � u [m ,n ] ≥ phigh % � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �
where plow% – lower percentile (in this case, 1%), phigh% –
the upper percentile (in this case 99%), the remaining
symbols are similar to the notation for the formula (7)
b −a
u p [m ,n ] = (u [m ,n ] − plow % ) ⋅
+a .
phigh % − plow %
Nor cut-off top and bottom 1% of the values, can use
3% and 5% thresholds and etc.
In this way, adjusting contrast noisiness on the tails
of the distribution will not have a significant effect on
the maximum and minimum for which will be scaled
intensities, so this method is more resistant to the presence of noise.
In addition to increasing the contrast, at the stage of
preparation of video image processing to the difference
motion detector, it is important to bring these images to
the same species. By this we mean a transformation of the
original image, after which the color intensity histogram
becomes uniform for all intensity values, which means
that all values are equally probable [6].
This effect is achieved by replacing the distribution
function of the pixel intensity of the color value with
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equal argument of the largest intensity by a factor of at
available to the number of levels of intensity:
k = u [m ,n ],
′
u [m ,n ] = (b − a ) ⋅ nk / N 2 ,
(9)
where, u[m, n] – pixel of the original image with the coordinates (m, n), u’ [m, n] – pixel of the output image
with coordinates (m, n), a – the lowest possible intensity value, b – the maximum possible value intensity, nk –
number of pixels in the original image with an intensity
level equal to or less than k, N – total number of pixels
in the image.
After treatment with this method, all frames of the
video stream will be similar in the level of illumination
(and the distribution of the number of pixels from the
values of the intensity will be close to a uniform) that will
prevent false alarms difference motion detection.
The method of the frame difference. Calculation of
frame difference is a very common method of detecting
the primary motion, after performing which, generally
speaking, we can say whether there is a flow of personnel
movement. Until recently, many motion detectors functioned exactly according to this principle [4]. However,
this approach gives a fairly rough estimate, leading to the
inevitable presence of the false reaction detector noise recording equipment, changing lighting conditions, a slight
swing of the camera and so on. Thus, the videos need to
be pre-treated prior to the calculation of the difference
between them.
Algorithm for computing frame difference of two
frames in the case of processing a color video in RGB
format is as follows:
1) The input to the algorithm receives two video
frames, which are two-byte sequence in RGB format.
2) calculates pixel inter-frame differences as follows:
Rdi = R1i − R2i
Gdi = G1i − G 2i (10)
Bdi = B1i − B2i
where Rdi ,Gdi , Bdi – values of red, green and blue color components of the i – th pixel of the resulting raster, R1i ,G1i , B1i ,
R2i ,G 2i , B2i – the values of red, green and blue color components of the i-th pixel in the first and second frame.
3) For each pixel the average value between the values of three color components:
z i = (Rdi + Gdi + � Bdi ) / 3 (11)
DETECTION AND DETERMINATION COORDINATES OF MOVING OBJECTS FROM A VIDEO
4) The average value is compared with a predetermined threshold. As a result, comparison of the binary
mask is formed:
0,� z i < T
(12)
mi =  i
1,� z < T

where mi – i – th value of the mask element, T – comparing the threshold, sometimes referred to as a threshold
or a sensitivity.
Thus, the output of the algorithm is formed by a binary mask, one element of which correspond to the three
color components of the corresponding source pixel two
frames. The units are arranged in the mask in areas where
possibly present motion at this stage, but can be separate
elements false alarms mask erroneously set to 1.
The two consecutive frames from the stream, but
may use frames with a long interval, for example, equal
to 1.3 can be used as a frame of the two input frames.
The greater this interval, the higher the sensitivity of the
detector to the inactive objects, experiencing only a very
small shift in one frame and can be clipped, being ascribed to the noise component of the image.
The advantage of this method is its simplicity and
undemanding to computing resources. The method is
widely used earlier on the grounds that there was insufficient processing power available to developers. However,
it is now widely used, especially in multichannel security
systems where it is necessary to process the signal from
multiple cameras to one computer. After all, the complexity of the algorithm is of order O(n) and is carried
out in just one pass, which is very important for a large
raster dimension, such as 640 × 480 pixels, 768 × 576
pixels, with which modern video cameras often work.
Results. Besides the fact that the implemented algorithm allows to process data in real time and to provide
them moving objects, it has one very important feature.
It lies in the fact that the program is written on the basis
of this algorithm, it is a full-transforming the DirectShow
filter. This means that, firstly, it is compatible with other
DirectShow-filter and can be included in a filter graph, and
secondly, it can easily be used by any application in which
to detect movement in the video data. To this end, the
program developers need only to know the unique identifier of the class and interfaces used in this filter detects
motion. Then, after the resulting interface program can
communicate with the connected to the column filter setting adjustable parameters and thus realizing its setting.
The scheme used algorithm detail to the level of
processing procedures, and input / output parameters
is shown in (Figure 1).
Figure 1. Stages of the modules
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Section 11. Technical science
The algorithm consists of the following steps:
Step 1. Saved K‑2 (K‑2 frame is)
Step 2. Saved K‑1 (K‑1 block)
Step 3. Saved K – (current frame)
Step 4. Ref. Frame K‑1 (base frame)
Step 5. Ref. Frame K (updated base frame)
Step 6. Mask_B (mask on the difference between the
current and the base frame)
Step 7. Rectangles (an array of rectangles flanking
group of connected minzon)
Step 8. Objects K‑1 (an array of objects from the previous frame)
Step 9. Objects K (array of objects after the current
frame processing)
The following notation is used for operations:
•
•
•
•
•
•
•
•
Interpolate – procedure of frame filtering
Minus – the difference between the two
Morphology – performs filtering operations of
mathematical morphology
SearchRetangles – search boxes on the difference
mask
ProcessObjects – the search for new objects and
tracing old
PostProcessObjects – removal of unnecessary
objects
SetRectAreaValue (rectangles, map) – marks the
pixels belonging rectangles rectangular area on
the specified map pixel map
UpdateRef Frame – updates the pixels of the
base frame.
Figure 2. Software for Detection and determination of the coordinates of moving objects on the video
When pressing the button shows Algoritm algorithm
preprocessing and detection of a moving object (Figure 4).
The software is developed in language Delphi 7. The
software is installed on a computer running a surveillance camera.
Software removes the images in the format *.BMP
of video over a range of time and sequences of images
supplied to the algorithm.
The program contains both the original versions of
functions that implement these operations, and optimized the use of which is possible if you want users to
speed up the work of the detector and if it has a processor
not lower than Intel Pentium IV.
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Discussion. During the reviewed methods of image
processing and detection of moving objects in a stream of
video frames. For a number of methods have been implemented algorithms allowed to apply these techniques in
the detector of moving objects.
Based on implemented algorithms was developed
detection of moving objects. Designed detector is characterized by high quality of detection, resistance to noise
recording equipment, peculiarities of weather conditions, the ability to work in daylight and artificial light,
high-speed processing provides 1 channel video with a
resolution of 640 × 480 pixels at a speed of 8–10 frames
per second.
DETECTION AND DETERMINATION COORDINATES OF MOVING OBJECTS FROM A VIDEO
Figure 3. Formation of the report as a spreadsheet
Figure 4. Results of frame difference and median filtering
When implementing modern powerful detector
technology have been used, such as the COM and DirectShow, optimization was performed bottleneck algorithm using MMX instruction sets for SSE and acceleration of its operation.
At all stages of the development work carried out
thorough testing of individual parts of the algorithm, as
well as the entire detector as a whole.
Conclusion. Detecting and tracking moving objects
are important topics in computer vision research. Classical detecting and tracking methods for steady cameras
are not suitable for use with moving cameras because the
assumptions for these two applications are different. In
this work developed algorithms that can detect and track
moving objects with a non-fixed position camera. The
initial step of this research is to develop a new method
for estimating camera motion parameters.
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Section 11. Technical science
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