Overview Of Video Object Tracking System

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International Journal of Modern Trends in Engineering
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e-ISSN: 2349-9745
p-ISSN: 2393-8161
Overview Of Video Object Tracking System
Ms.Kavita Borse1
, Prof.Rupali Nikhare2
1,2
Computer Engineering, Pillai Institute of Information Technology, Mumbai
Abstract— The goal of video object tracking system is segmenting a region of interest from a video
scene and keeping track of its motion, positioning and occlusion. There are the three steps of video
object tracking system those are object detection, object classification and object tracking. Object
detection is performed to check existence of objects in video. Then the detected object can be
classified in various categories on the basis on their shape, motion, color and texture. Object tracking
is performed using monitoring object changes. This paper we are going to take overview of different
object detection, object classification and object tracking techniques and also the comparison of
different techniques used for various stages of tracking.
I. INTRODUCTION
Videos are the sequences of images or frame which are displayed in fast frequency so that human
eyes can visualize the continuity content. All image processing techniques can be applied to
individual frames. In addition to, the contents of two consecutive frames are usually closely related.
There are basically, three steps of object tracking which are given in Figure.
Figure 1. Basic Step for video object Tracking
A. Object Detection
Object Detection is to identify objects in the video sequence and cluster pixels of these objects.
Object detection can have various techniques such as frame differences, Optical flow and
Background Subtraction.
B. Object Classification
After the object detection we have to classify the object on the basis of their shape, motion, color and
texture. There are some approaches of classification methods such as Shape-based classification

International Journal of Modern Trends in Engineering and Research (IJMTER)
Volume 01, Issue 05, [November - 2014] e-ISSN: 2349-9745, p-ISSN: 2393-8161
method, Motion-based classification method, Color based classification method and Texture based
classification method.
C. Object Tracking
Track or observe the moments of a particular object in every frame. The approaches to track the
objects are point tracking, kernel tracking and silhouette based tracking.
The challenges that should be taken care in video object tracking are described below:
 Noise in an image
 Difficult object motion
 Imperfect and entire object
 Occlusions Complex objects structures
Now we are going to discuss all these three steps in detail.
II. OBJECT DETECTION TECHNIQUE
First step in the process of object tracking is to identify objects of interest in the video sequence and
to cluster pixels of these objects. Find the region of interest of user. Detailed explanation for various
methods is given below.
A. Frame Differencing
In this technique the moving objects is determined by calculating the difference between two
consecutive images or frames. As shown in Figure the result of object detection using frames
difference method.
Figure 2. (a) Original Frame Figure 2. (b) Frame Difference Method
Frame Differencing deals with:
 Its calculation is simple
 Easy to implement
 For dynamic environments, it has a strong adaptability, but it is generally difficult to obtain
complete outline of moving object, as a result the detection of moving object is not accurate
B. Optical Flow
Optic flow is a useful for tracking objects which are in motion. Optical flow method is to calculate
the image optical flow field and then do clustering processing according to the optical flow
distribution characteristics of image. This basic method in this context is called optical flow, which
reflects the image changes during a time interval
Optical Flow deals with:
 Using this method we can get the complete movement information.
 It detect the moving object from the background better
 Require the large quantity of calculation
 Sensitivity to noise
 Poor anti noise performance

 It is not suitable for real-time demanding occasions
C. Background Subtraction
First step for background subtraction is background modeling. Background Modeling is to yield
reference model. The reference model is used in background subtraction in which each video
sequence is compared against the reference model to determine possible Variation or changes in the
frames. The variations between current video frames to that of the reference frame in terms of pixels
signify existence of moving objects. Figure shows the Result of background subtraction.
Figure 3. Result of Background Subtraction
Background Subtraction deals with:
 Simple algorithm
 It is very sensitive to the changes in the external environment
 Poor anti- interference ability
III. OBJECT CLASSIFICATION
The detect moving region may be different objects such as vehicles, birds, humans, floating clouds,
swaying tree and other moving objects.
A. Shape-Based Classification
It is simply a pattern matching. The different descriptions of shape information such as
representations of points, box and blob are available or stored for classifying moving objects. Input is
a mixture of image-based and scene-based object parameters such as image blob area, box
.Classification is performed on each blob or region at every frame and results are kept in histogram.
B. Motion-Based Classification
To detect the moving object motion based classification used. Optical flow is also useful for object
classification. Residual flow can be used to analyze rigidity and periodicity of moving entities.
C. Color-Based Classification
Color is relatively constant under viewpoint changes and it is easy to be acquired but color is not
appropriate for detecting and tracking objects, but for the low computational cost of the algorithms
proposed makes color a desirable feature to exploit when appropriate. To detect and track vehicles in
real-time color histogram based technique is used.
D. Texture-Based Classification
Texture based technique counts the occurrences of gradient orientation in localized portions of an
image is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast
normalization for improved accuracy. Texture is a degree of intensity dissimilarity of a surface

which enumerates properties such as smoothness and regularity.
IV. VIDEO OBJECT TRACKING METHODS
The purpose of an object tracking is to generate the route for an object above time by finding its
position in every single frame of the video. The jobs of detecting the object and creating
correspondence between the object occurrences through frames can either be accomplished
separately or jointly. In the first stage, Region of interest (ROI) in each frame is determined by
means of an object detection algorithm and then tracking corresponds to objects in every frame. In
final stage, the object region is projected by iteratively updating object location obtained from
previous Frames. Object Tracking is generally categorized as:
Figure 4. Categorization of Object Tracking
Now we are going to discuss in detail about each tracking approaches and their functionality of
object tracking, merits and demerits.
A. Point Tracking Approach
In this, the moving objects are represented by their feature points during tracking. Point tracking is a
complex problem particularly in the incidence of occlusions and false detection of object.
Point Tracking deals with:
It is simple
Useful for tracking very small objects.
Some approaches based on point tracking are as follows:
 Kalman Filter
Kalman filter is based on Optimal Recursive Data Processing Algorithm. In other words, they are
tracked based on the criteria chosen to evaluate performance. Optimal point will be taken based on
criteria that make sense.
Mean Shift Method
Support Vector
Machine
Layering Based
Tracking
Shape Matching
Particle Filtering
Multiple Hypothesis
Tracking
Kalman Filtering
Point Tracking
Object Tracking
Simple Template
Matching
Kernel Tracking
Contour Tracking
Silhouette Tracking

The Kalman Filter performs the restrictive probability density propagation. They composed of two
phases, prediction and correction. Prediction of the next state using the current set of observations
and update the current set of predicted measurements. The second step is gradually update the
predicted values and gives a much better approximation of the next state. Showed clearly in figure.
Figure 5.Basic Steps of Kalman Filter
The result of kalman filter is as shown in figure 6. (a) Shows the initial position of frame and figure
6. (b) Shows the result of kalman filter.
Figure 6. (a) Original Frame Figure 6. (b) Result of Kalman Filter Method
Kalman Filter deals with:
 It gives optimal solutions.
 Handling noise
 Tracking is applicable only for single
 Particle Filter
This generates all the models for one variable before moving to the next variable. Algorithm has an
advantage when variables are generated dynamically and there can be confoundedly numerous
variables. It also allows for new operation of re sampling. One restriction of the Kalman filter is the
assumption of state variables are normally distributed (Gaussian). Thus, the Kalman filter is poor
approximations of state variables which do not Gaussian distribution. This restriction can be
overcome by the particle filtering. It also consists of two phases: prediction and update as same as
Kalman Filtering.
 Multiple Hypothesis Tracking (MHT)
If motion correspondence is recognized using only two frames, there is always a limited chance of an
incorrect correspondence. Better tracking outcomes can be acquired if the correspondence choice is
overdue until several frames have been observed. The MHT algorithm upholds several
correspondences suggestions for each object at each time frame the final track of the object is the
most likely set of correspondences over the time period of its observation.
MHT is an iterative algorithm. Iteration begins with a set of existing track hypotheses. Each
hypothesis is a group of disconnect tracks. each hypothesis, a prediction of object’s position in the
succeeding frame is find and the predictions are then compared by calculating a distance measure.
Multiple Hypothesis Tracking deals with:
 Tracking multiple object
 It also handles occlusions.
Prediction of State
Variable
Update the Predicted
State Variable

 Calculating of optimal solutions.
B. Kernel Based Tracking Approach
Kernel tracking is usually performed by computing the moving object, which is represented by
geometric shapes like rectangle and ellipse. But one of the restrictions is that parts of the objects may
be left outside of the defined shape while portions of the background may exist inside. This can
detect rigid and non-rigid objects .They are large tracking techniques based on representation of
object, object features ,appearance and shape of the object.
There are a variety of tracking methodologies present based on this Kernel tracking approach:
 Simple Template Matching Method
Template matching is a brute force method of examining the ROI in the ongoing video, a simple way
of tracking wit reference image. Here in template matching, a reference image is verified with the
frame that is separated from the video. It can track only single object in the video. Translation of
motion only can be done in template matching. As shown in figure here the reference image is taken
from the video and they are compared with the successive Frames in the video.
.
Figure 7.Simple Template Matching
Simple Template Matching deals with:
 Tracking single object.
 Partial occlusion of object
 Mean Shift Method
The task is to first define an Region of Interest (ROI) from moving Object by segmentation and then
tracking the object from one frame to next. Region of interest is defined by the rectangular window
in an initial frame. Tracked object is separated from background by this algorithm. The accuracy of
target representation and localization will be improved by Chamfer distance transform. Minimizing
the distance among two color distributions using the Bhattacharya coefficient is also done by
Chamfer distance transform. In tracking an object, we can characterize it by a discrete distribution of
samples and kernel is localized.
Steps for Mean shift tracking
Probabilistic distribution of target in first frame is obtained using color feature.
Compare the distribution of first frame with consecutive frame.
Bhattacharya coefficient is used to find the degree of similarity between the frames.
Loop will continue till the last frame.
Mean Shift Method deals with:
 Tracking only single object.
 Object motion by translation and scaling.
 Necessity of a physical initialization.
 Object is partial occlusion.

In the Figure 8. (a), the initialization or selection of the object of interest was performed. After that
Figure 8. (b), (c) show how the Mean-shift algorithm tracks the vehicle. It shows that the tracking
window is not centered on the object of interest. Then we can observe that this tracking window lost
its object. This is also evident by referring to Figure 8. (c).
Figure 8. (a) Initialize ROI Figure 8. (b) Start to Track the Object
Figure 8. (c) Mean Shift Method Lost ROI
 Support Vector Machine (SVM)
SVM is a broad classification method which gives a set of positive and negative training values. For
SVM, the positive samples contain tracked image object and the negative samples consist of all
remaining things that are not tracked. During the analysis of SVM, score of test data to the positive
class.
As shown in figure SVT takes as input the initial guess of the position of the vehicle (dashed
rectangle) and finds the position with the highest SVM score (solid rectangle).
Figure 9.Support Vector Machine (SVM)
Support Vector Machine is capable of dealing with:
 Tracking single image
 Partial occlusion of object
 Necessity of training
 Object motion by translation
 Layering Based Tracking
This is another method of kernel based tracking where multiple objects are tracked. Each layer
consists of shape representation such as ellipse, rectangle, and motion such as translation and rotation
and layer appearance based on intensity.
Layering Based Tracking deals with:
 Tracking multiple images
 Fully occlusion of object

 Object motion by translation, scaling and rotation
C. Silhouette Based Tracking Approach
Objects having composite shapes such as hands, head, and shoulders, are cannot defined by
geometric shapes. Silhouette based approaches will afford a perfect description of shape for those
objects. The aim of a Silhouette based object tracking is to find the object region by means of an
object model .This model is verifying the object region in each frames. Model can be represented by
a histogram, object edges or contour.
Silhouette tracking classify into two categories, contour tracking and shape matching.
 Contour Tracking
Contour tracking methods, in divergence to shape matching methods, iteratively develop an original
contour in the foregoing frame to its new position in the present frame, overlapping of object
between the current and next frame. Contour tracking is in form of State Space Models. State Space
Models: State of the object is named by the parameters of shape and the motion of the contour. The
state is updated for each time according to the maximum of probability.
In Contour Tracking, explicitly or implicitly are used for the representation on silhouette tracking.
Representation based on explicitly will defines the boundaries of silhouette whereas in case of
implicitly, function defined by grid. Contour evolution: iteratively evolve an initial contour in the
previous frame to its new position in the current frame. This technique or method requires that some
part of the object in the current frame overlaps with the object region in the previous frame.
Contour Tracking is capable of dealing with:
 Handling of large variety of object shapes easily
 Handling Occlusion
 Dealing with object split and merge
Figure 10. Contour Tracking
D. Shape Matching
These approaches examine for the object in the existing frame. Shape matching is similar to the
template based tracking in kernel tracking approach. Shape matching is to find matching silhouettes
detected in two successive frames. Silhouette matching is similar to point matching. in the Silhouette
tracking detection is based on background subtraction. Object are in the form silhouette boundary or
object edges.
Shape Matching is capable of dealing with:
 Edge based template, Silhouette tracking feature of shape matching are able to track only
single object.
 Occlusion handling performed in with Hough transforms techniques.

Figure 11. Shape Matching
IV. COMPARISION OF ALL TRACKING TECHNIQUES
The advantages and disadvantages of different video object tracking methods is as shown in the
Table 1. The parameters for the comparison are category, number of object tracking, optimal result
and need of training rules.
Table 1. Comparison of the Various Video Object Tracking Methods
Sr.
No
Methodology Category Number of
Object
Tracking
Occlusion Optimal
Result
Need of
Training
Rules
1 Kalman
Filter
Point
Tracking
Single No Yes No
2 Particle
Filter
Point
Tracking
Multiple Yes Yes No
3 MHT Point
Tracking
Multiple Yes Yes No
4 Template
Matching
Kernel
Tracking
Single Partial No No
5 Mean Shift Kernel
Tracking
Single Partial No No
6 SVM Kernel
Tracking
Single Partial No Yes
7 Layering
Based
Tracking
Kernel
Tracking
Multiple Full No No
8 Contour
Matching
Silhouette
Tracking
Multiple Full Yes Yes
9 Shape
Matching
Silhouette
Tracking
Single No No No
V. APPLICATIONS
Some of the tracking applications are:
A. Computerized video surveillance
The movements or action in particular an area are monitored by automated vision system.
B. Robotic vision
To recognize different obstacles in the path to avoid overlapping. Point tracking method is
used in the object vision.
C. Traffic monitoring
In specific countries highway traffic is constantly observed using cameras. The surveillance

system is supported by an object tracking system, to identify the breaking rules made of
vehicles or any other unlawful act. Here the optic flow object tracking method is used.
D. Animation
Object tracking algorithm can also be prolonged for animation.
E. Gesture Identification
Identification of human parts like eye, hand, and face etc.The contour matching method is
used.
IV. CONCLUSION
Object is tracked mainly on the bases of object detection, object classification, tracking and decisions
about activities. We mainly classifies object tracking approach as point tracking, kernel based
tracking, and silhouette based tracking. In the object detection, Frame differencing, optical flow and
the background subtraction methods, Frame differencing provide High accuracy, Low computational
time also it is the Easiest Method and Perform well for static background.
In the object classification, Texture based and color based are widely used because they provide
higher accuracy and Provides improved quality with the expense of additional computation time. In
the tracking technique the point trackers involve detection in every frame, while geometric area or
kernel based tracking or contours-based tracking require detection only when the object first appears
in the scene. Contour based Tracking will track multiple object with fully overlapping and flexibly
also it handle the occlusion and provide the optimal result.
REFERENCES
[1] Himani S. Parekh,Darshak G. Thakore,Udesang K. Jaliya,“A survey on object detection and tracking
Methods”,International Journal of Innovative Research in Computer and Communication
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Methods”,International Journal of Advanced Research in Computer Science and Software Engineering,
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[3]Sandeep Kumar Patel,Agya Mishra,“Moving Object tracking Techniques:A Critical Review”,Indian
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[4]Mr. Joshan Athanesious J , Mr. Suresh P,“Implementation and Comparison of Kernel and
SilhouetteBased Object Tracking”,International Journal of Advanced Research in Computer Engineering &
Technology (IJARCET),Volume 2,Issue 4, pp.1298-1303,2013.
[5]N. Prabhakar, V. Vaithiyanathan, Akshaya Prakash Sharma, Anurag Singh and Pulkit Singhal,“Object
Tracking Using Frame Differencing and Template Matching”,Research Journal of Applied Science,
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[6]Shehan Fernando, T M J A Cooray,“Mean Shift Kalman Object Tracking for Video
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Overview Of Video Object Tracking System

Overview Of Video Object Tracking System

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Overview Of Video Object Tracking System