IRJET- Real Time Video Object Tracking using Motion Estimation

© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 959
REAL TIME VIDEO OBJECT TRACKING USING MOTION ESTIMATION
Ms. Pranali Atul Pawar
Ms. Pranali Atul Pawar, Badlapur lecturer, Dept. of computer Engineering, Vpm’scollege, Maharashtra, India
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Abstract – Real time object tracking is considered as a
critical application. Object tracking is one of the most
necessary steps for surveillance, augmented reality, smart
rooms and perceptual user interfaces, video compression
based on object and driver assistance.
The proposed method, efficient motion detection and object
image capturing based on background subtraction using
dynamic threshold approach with mathematical
morphology. Here these different methods are used
effectively for object detection and compare these
performance based on accurate detection.
Here the techniques frame differences, dynamic threshold
based detection will be used. After the object foreground
detection, the parameter of coordinates will be determined.
For this, most of previous methods depend on the
assumption that the background is static over short time
periods. In dynamic threshold based object detection,
morphological process and filtering also used effectively for
unwanted pixel removal from the background. The
background frame will be updated by comparingthecurrent
frame intensities with reference frame. Along with this
dynamic threshold, mathematical morphology also used
which has an ability of greatly attenuating color variations
generated by background motions while still highlighting
moving objects.
Finally the results will be shown that used approximate
median with mathematical morphologyapproachiseffective
rather than prior background subtraction methods in
dynamic texture scenes and performance parameter.
Key Words: Contour, Motion Detection, Object Detection,
Object Tracking, Shape Features
1. INTRODUCTION
Video object tracking has got wide application in vision,
security, observational issues in natural science and in
various other fields.
Video surveillance for security purpose is one of its major
applications. Object tracking has high priorities in religious
places, market buildings, courts, train stations and airports.
Various other applications includemilitary,astronomy,road
traffic regulation, robotics, medical imaging. Air traffic
control is a typical application of video object tracking,
where aircraft are more or less continuously visible on
radar, but in case the transponders are absent the identity is
only revealed when the pilot reports by radio.
Video analysis basically involves three key steps:
To detect the moving object under interest.
1) Track the object from frame to frame
2) Analyse the object tracks to know their behaviour.
In simple words, tracking may be defined as the
estimation of the trajectory of a moving object in the image
plane as it moves around the scene.
Consistent labels are assigned to the tracked objects in
each frame of a video. Further based on the tracking domain,
a tracker can give useful informationsuch as area,shape,and
orientation of the object under interest.
Object tracking can be complex due to following reasons -
 Noise in images,
 Complex object motion,
 Articulated nature of non-rigid objects
 Scene illumination changes, and
 Real-time processing requirements.
1.1CLASSIFICATION OF REAL TIME OBJECTDETECTION
USING MOVING ESTIMATION TECHNIQUES:-
1.2.1 THRESHOLDING
Thresholding is based on clip-level or value to turn a gray-
scale image into a binary image. It is the simplest method
used for image segmentation. This method is carried out by
first selecting a threshold value (or values in case of
multiple-levels are selected) which is optimum. In industry
several popular methods are used, including the maximum
entropy method, Otsu’s method (maximum variance) etc.
1.1.2 BACKGROUND SUBTRACTION:
We use only the successive I-frames for tracking in our
algorithm and thereafter interpolatetheobjectmotioninthe
intermediate frames. We initially acquire a DCT image of an
I-frame representing the background that is treated as the
reference image. After that, all subsequent DCT images are
compared with the reference image to segment the
foreground object. Based on the model of the applicationthe
background image is created and is updated from time to
time whenever there is a permanent change in the
background.DetailsregardingThresholdingandBackground
Subtraction method have been described later.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 08 | Aug 2018 www.irjet.net p-ISSN: 2395-0072

1.1.3 BACKGROUND ESTIMATION:
In this technique the algorithm identifies the incomplete
background as those pixels that do not belong to the
foreground pixels. As the foreground objects move, the
background estimation algorithm estimates more and more
of the background pixels. Once background estimation is
completed by the program, the background is subtracted
from each video frame to produce foreground images. This
foreground image is converted to binary image. This is
carried out by implementing thresholding and performing
blob-analysis and other morphological closing on each
foreground image. Then object tracking is carried out by
another program.
1.1.4 OPTICAL FLOW:
The optical flow of a video frame is a field of motion vector
per pixel or sub-pixel. Multiplemethodsallowcomputing the
optical flow among which partial differential equationbased
methods, gradient consistency based techniques and least
squared methods.
In this model we have used an optical flow estimation
technique to get an estimation of the motion vectors in each
frame of the video sequence. Then the required moving
object is detected by a program block and converted into
binary image. This is carried out by implementing
thresholding and performing blob-analysis and other
morphological closing on each foreground image. Then
object tracking is carried out by another program.
1.2 OBJECTIVE
Detecting regions that corresponds to moving objects in
video sequence plays a very important role in many
computer vision applications.
In simplest form, Object detectionfromvideosequenceis the
process of detecting the moving objects in frame sequence
using digital image processing techniques. Moving object
detection is the basis of moving object identification and
tracking.
In the past two decades object detection and tracking in
video is a challenging problem and has been extensively
investigated. It has applications in various fields such as
video compression, video surveillance, human-computer
interaction, video indexingandretrieval etc.Objectdetection
involves locating object in the frames of a video sequence,
while object tracking represents the process of monitoring
the object’s spatial and temporal changes in each frame.
Object detection can be performed through region-based
image segmentation, background subtraction, temporal
differencing, active contour models, and generalized Hough
transforms. In order to allow high-resolution images of the
people in the scene to be acquired it is reasonable to assume
that such people move about in the scene.
2. TECHNIQUES AND ALGORITHUM
In computer vision, segmentation is the process which
divides a digital image into multiple segments(setsofpixels,
also known as super-pixels). The main aim of segmentation
is to simplify and/or change the representation of an image
frame into something which is more meaningful and easier
to analyze. Segmentation of image is typically used to
detect/locate objects and boundaries (lines, curves, etc.) in
an image frame. More precisely, segmentation of an imageis
carried out to assign a label to each pixel in an image such
that pixels with the similar label share certain visual
characteristics.
Fig.1 A Generic Framework for Video Processing
Algorithm
Morphological Process
Morphological operations are applied on segmented binary
image for smoothening the foreground region. It processes
the image based on shapes and it performs on image using
structuring element. The structuring elements will be
created with specified shapes (disk, line, square) which
contains 1’s and 0’s value where ones are represents the
neighbourhood pixels. Dilation and erosion process will be
used to enhance (smoothening) the object region by
removing the unwanted pixels from outside region of
foreground object. After this process, the pixels are applied
for connected component analysis and then analysis the
object region for counting the objects.
Code:- b = bwmorph(diference,'close');

Morphological operations on binary images.
BW2 = bwmorph(BW,operation)
BW2 = bwmorph(BW,operation,n)
gpuarrayBW2 = bwmorph(gpuarrayBW,___)
Examples
1) Perform Morphological OperationsonBinaryImage
Read binary image and display it.
BW = imread('circles.png');
imshow(BW);
Fig.2 show object with background
Remove interior pixels to leave an outline of the shapes.
BW2 = bwmorph(BW,'remove');
Figure imshow(BW2)
Fig.3 show object with only boundaries
Fig.4 shows the example of morphological operation.
ALGORITHM USED:
A pixel is marked as foreground if where is a “predefined"
value threshold. The process thresholding is followed by
closing with a 3 X 3 kernel and the discarding of small
regions. The background is updated as where the value is
kept small to prevent the detection of artificial “tails"
forming behind moving objects.
Two background corrections are applied:
1. If a pixel is marked as foreground for more than m of the
last M frames, then the background is updated. This
correction is designed to compensate for sudden
illumination changes and the appearance of static new
objects.
2. If a pixel change is frequent that it changes its state from
foreground to background frequently, it can be masked out
due to inclusion in the foreground. This is designed to
compensate for fluctuating illumination, such as swinging
branches of trees.
Fig -5: System Architecture

FLOW OF THE PROCESS
Figure.. Flow of the Process
Flow of the System
Step 1: Idle- capture image (static or dynamic)
Step 2: Image acquisition- load the image in system
Step 3: Object detection- detect the different objects in the
video or image
Step 4: Object tracking- track Object and compare with each
earlier frame
Step 5: Capture it in system with given path
Step 6: Count frames and give the numbering
Step 7: It store in the path
Step 8: Get the differences
Step 10: Final result
3. CONCLUSIONS
The objective has been to detect moving objects and
thereafter, decide on objects of particular interest which
would be tracked. While earlier we worked with object-
intrinsic properties such as the centroid of a moving object
in order to make a probable prediction of its immediate
future motion, methods to detect a rectangular boundaryfor
the object, then used background estimation method using
Simulink models and got fair output for single object, butwe
did not obtain satisfactory results when the methods were
worked with multiple objects. Further we made an attempt
using the Optical Flow method wherein the morphological
algorithm for motion estimation was put into effect. The
latest method of Adaptive Contrast Change Detection gave
satisfactory results in sufficiently reducing the noise while
detecting multiple objects. But in some cases it gives
unwanted noise. Hence, we have used correlation which
basically gives the relation between to frames having
significant contrast change. Use of correlation has
significantly improved the output and gives better result
even with multiple moving objects. The approach seems to
have efficient practical applications in poorly-lighted
conditions such as night-time visual surveillance systems.
REFERENCES
[1] Kasra Rezagholipour, Mohammad Eshghi,” Video
Steganography Algorithm based on motionvector of moving
object”[2][3], 2016 Eighth International Conference on
Information and Knowledge Technology (IKT), Hamedan,
Iran.
[2] P. Bhuvaneswari, T. Siva Kumar,” MovingObjectTracking
using Background Subtraction Techniqueand itsParametric
Evaluation”[4], International Journal of Advanced Research
in Electronics and Communication Engineering (IJARECE)
Volume 3, Issue 9, September 2014.
[3] Sherin M. Youssef, Meer A. Hamza, Arige F. Fayed,
"Hybrid wavelet-based video tracking using adaptive
contrast change detection in night-time visual surveillance
systems"[7], WEC 2010, vol.2183, pp. 732 – 737
[4] Takanori Yokoyama, Toshiki Iwasaki, and Toshinori
Watanabe Graduate School of Information Systems,
University of Electro-Communications, 1-5-1 Chofugaoka,
Chofu-shi, Tokyo 182-8585, Japan,” Motion Vector Based
Moving Object Detection and Tracking in the MPEG
Compressed Domain”[1], 2009 Seventh International
Workshop on Content-Based Multimedia Indexing.
[5] Huanga K., Wanga L., Sana T., Tana T., Maybankb S., “A
real-time object detecting and tracking system for outdoor
night surveillance” [3], PatternRecognition, ELSEVIER2008,
vol.41, pp. 432-444

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