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In is a need to have high-quality methods

In modern life, we see many
challenges and problems associated with the buildup of technology. Multi-object
tracking is non-different from this. Ever since the terrorist attack that
happened in New York people wanted better security. Indeed, the surveillance
cameras to provide the efficient
security, but there is a need to make it better by decoding its algorithms. They
desired these cameras to focus on detecting humans with suspicion more
accurately.

 

There is a need to have high-quality methods for getting the better
accuracy at targeting wrongdoings. People
have developed and proposed multiple ways for this but that was not efficient.
Till now cameras are capturing only one single target.

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People then proposed the idea of
having a multi-camera system to capture the
actions of one single person. For this, they will need different kinds of
algorithms to extract some important features of the target so that when the
same person appears again, the camera will recognize them.

 

The computer representation of
the tracking objects should be made for a proper
approach towards multi-object tracking. There have been developed many
strategies for that but this answer will cover the most important aspect of
required and possible solutions for this problem.

 

The
tracking problem.

 

By definition, the multi-object
tracking is a system that captures the configuration of multiple moving or
non-moving objects and determines separate identities in various frames one by
one. Many professionals are working to solve this multi-tracking problem by applying two primary approaches. The
first is when information is taken from inter-camera and second is associated
with input detection in the global phenomenon.

 

Before that, the important topics
for understanding are activity recognition and object recognition, tracking,
and detection. The rear is used to find the configurations and locations of the
captured objects belonging to a particular type. Recognition refers to the task
to identify objects with respect to the type of class it belongs to. The
classic example can be a car that has been identified by its manufacturer.

 

The tracking system is the
combination of the other two explained aspects of this system. The focus is
kept on identifying the frames by drawing correspondences from one frame to
another. This is usually done so that they can have consistent labels.

 

The ending task is to recognize
the activity over a period of time while
ignoring the fact that it is quite similar to the previous one. The only
difference one could make out is that the tracking follows continuous frames
whereas the end task is done to focus on one goal. This follows the tracking observations
in which people recognize activities that are often more complex than
previously done.

 

A good example is a handbag that
has been left behind by a person and it has been recognized by the surveillance
cameras.

 

Tracking
in offline and online systems.

 

The online tracking refers to the
process in which only the past and the information of the present are available
to process and get the results. The other one refers to the collection of
information to be viewed even without the access to the internet. This tracking system on online management is said to be real-time tracking in common terms. This system
gives the result of tracking almost instantly as it arrives.

 

There is a vast difference
between all the above-mentioned methods
that is online, real-time and offline.
Usually, the offline technique is not
quite useful for interactive purposes. It cannot produce quicker results.

 

The offline method is only used
when for the collection of traffic statistics and other related methods such as
video indexing. The offline system can
improve the tracking system because they can access saved videos on their hard drives.

 

Offline methods can be effective over the online technology since the time constraints in
the former do not exist. But there’s a catch, even online systems can act as if
they are standalone offline systems. This can be done by providing time delay
mechanisms. There are many models in multiple tracking systems that are managed
online. But few discussed below are still online systems.

Difficulty in the tracking
system.

 

Object
modeling is the most problematic aspect multi-object tracking system. The
computers still cannot define the identity
of an object with respect to computers. The good example is the tracking
program for a dog. If the programmer
desires to track a certain dog, he should program such a system by giving
detailed descriptions of that particular dog that differs from other objects.
The color, size, shape, breed and any birthmark
of the dog should be clearly described. He should be careful enough to avoid
making mistakes as much as possible. If something goes wrong, the program will
identify a cat as a dog.

 

This marks the
multi-object tracking little more difficult. The system should be able to deal
with the visual description of a particular object in an efficient way. In
object modeling, each and every object captured should be recognized in the correct and effective way. The varieties
of aspects may differ from textures, shapes, motion, background, and color.

 

Challenges in the generic
tracking system.

 

Even the
system successfully performs the object modeling function, the problem arises
when the appearance of the captured object changes, like a thief wearing a
different attire to fool the officials. One way the object modeling can tackle
this problem is by constructing 3D models and having a collection of few
templates based on appearance.

 

The second
method for this is by changing the shape
directly. Nonmoving objects are easy to track and identify. But humans change clothes every day, they deform. Such objects
require a much complex mechanism of the algorithm to track them effectively. The change
of hairstyle is a good example in this
case. This needs a high level of model preparation to identify high dimensional
objects.

 

Furthermore,
the objects that far from the camera can appears smaller in size. This creates
one more hindrance in the modeling of the object. A small variation in the focused object will be harder to identify.

 

Another
problem in object modeling may appear from illumination changes. It means if
the object appears in the harsh sun rays can become difficult to identify in the
night time with different appearance and looks. Objects vary in various aspects
of reality such as intensity and color that makes this modeling of objects more
difficult. The reflection of light on the particular object is also another
source of the problem.

 

Reflections
cause the object modeling techniques to appear more hazardous. Shadows may seem
similar to the focused object but it varies in certain aspects like background,
shape, texture and even motion. In short, the variations in shadows and
reflections can bewilder the modeling process due to its nature of constant
change.

 

Another
problem comes when another object covers the image of focused one. This is
called as an occlusion in common terms. The images before and after the
occlusion take place should be compared
thoroughly to gaining an insightful
result.

 

Tracking challenges of multi-objects.

 

The above
challenges and problems refer to the single
tracking system. But multi-object tracking has some issues too. First is
to represent such a modeling system that tracks multiple objects at one time. People
have proposed two methods. First is to have a unique representation of each object
in the scene and second is to have individual configuration details.

 

Distinguished tracking
of similar objects and classes becomes difficult because of similar texture, color or shape. Appearance and classes
matter but those of similar conditions make it quite difficult for tracking. This
creates another problem in the tracking of multiple objects.

 

Data association becomes another problematic situation in the tracking system. A multi-object is to be tracked by assigning a measurement of a
particular form and estimating the various tasks at the same time.

 

Tracking problem of multiple objects.

 

A solution to
all tracking problems.

 

Today the
solution is easy to find given that we are surrounded by efficient
technological materials. But it might take some time since the search space is
big enough to take much of an individual’s time.

 

Online methods
usually use real-time operations and applications.
And thus, they require fast processing techniques. Object representation,
however, can give solutions to the problems by providing high dimensional
information. This to define the solution space becomes a mandatory task in this
field.

 

When one is
acquiring various search strategies, data ambiguity becomes another problem. There
may occur where people get various solutions to one problem of the tracking system and this can happen quite
often. The exact solution to this situation is that the solutions should be
analyzed and thoroughly check by applying various search strategy see if the solution
gives accurate results. Also, the search strategies should be performed in such
a way that the results could be beneficial globally to everyone.

 

Object tracking framework.

 

You can choose
appropriate paradigms by seeing various approaches for multi-object tracking
programs. The formulation of multiple object tracking system and its activity
takes place as a solution to all over problems.

 

There are
three varied phases of this:

 

Data acquisition:

 

The mere work
of this phase is to pass the collected information in another phase of the tracking system. This may involve data in one
camera or the cameras in the array. There
are few chances of the occurrence of many issues related to this phase of a tracking system such as data compression, image
sensing, camera placement, networking, synchronization etc.

 

The multiple object tracking:

 

The second
phase consists of further three distinctions of elements that overlap each
other. Those are environment modeling, search strategies, and object modeling.

 

Object modeling:

 

The object
modeling represents information of the data that is present in the visual form.
They use features of the focused object, as described earlier, which makes use
of various features such as shape, size, color, texture and etc. but in
multi-object tracking, the object modeling combines several features of the
focused object.

 

The environment
modeling relates the data, usually visual data, to the entire world. It means the 2D data is converted into 3D
information by gathering information from sources such as multiple cameras.

 

By using search
strategies, the environmental models and visual data are used by tracking
methods got gaining an accurate solution of the object.

 

Oftentimes tracking
methods are treated to be quite different from recognition and understanding of
behavior. And thus, the activity recognition process uses the other processes
as an observation tool.

 

Object modeling:

 

Information gained
from the visuals is used tin object mo0deling to represent various objects. Features
may vary from shape to shape or color to color. But the modeling processing to
detect objects is explained below.

 

Color modeling:

 

We remember
objects because of its color. It can distinguish one object to another and the
information is available in the videos. Sometimes the focused object is
detected by seeing the colors and matching them. The second approach for this
is to distribute the region and then the model of the color object is built. But
when it comes to similar objects, it becomes quite difficult to match them with
accuracy. The approach should be made to provide histogram representation of
each color model beside the real object regions.

 

Shape modeling:

 

The shape can vary from person to person
depending on the object. The object modeling system presents the
characterization by outlining edges and its domains to describe a geometrical aspect of the image. The model can
be allowed to be built by using the shape modeling and various categorizations
of it. Shapes are usually determined by deformable templates that vary
according to the difficulty to capture the image such as having to define lips,
eyes or nose of the particular face.

 

Texture modeling:

 

Just like
face, textures are used to identify the characteristics and aspects of a
particular object. It provides the information regarding the patterns that appear regularly. Jeans is a common example for this. There are
two ways to approach this: static modeling and spectral modeling. The first is
used for the collection of data from the images directly. And the second
involves in a computational way. After applying the filters to the image, the
modeling process then collects various statistics based on textures.

 

Motion modeling:

 

Motion detection
is an important phenomenon that is necessary for the tracking system. There is a need for
minimum two or three images of the scene to this method of tracking. The first approach
to this is by using optical flow methodology. The motions are represented as the
vectors that originate at pixels. There are many other methods as well that
have robust flow.

 

Background modeling:

 

Typically it
means to model the video objects by focusing on the background.it models the
appropriate appearance by drawing color, motion data, illumination etc. The easiest
approach is to have a distinction between two frames of the image. But the
method was unsatisfactory. There are, however, other methods as well that
indeed offers accurate results.

 

Multi-modal and others:

 

There is other approaches present that uses the combined aspects of features such as
shape, texture or color, background modeling, and
others to track efficiently.

 

Probabilistic approach:

 

This approach
for search strategy is flexible in a way to have an efficient and unique search
strategy for tracking the specific information needed. The learning of the
probabilistic models depends on the information learned during the process.

 

This approach
is entirely based on the representation of many unknown elements from the
visuals. Anyone can also use these elements in systematic ways which make the process quite flexible. The substitution
for every element is present and can be used.

 

This method is
systematic in a sense of tolerating the unpredictable targets with varieties of
features such as noise. It is also useful for fusing varieties of data having varied
sources of reality to make a certain approach.

 

One famous approach
is a non-Bayesian probabilistic method to
apply varied classifiers based on the kernel
and uses it for tracking details. Another similar approach is made, known as
the recursive Bayesian approach that is used to model the tracking problem
using Dynamic Bayesian Network, which is an acyclic
graph that represents different edges and variables related to the
condition.

 

Non-probabilistic approach.

 

This section
of the strategic search for the
betterment of tracking model deals with the environment models, object models,
image data, etc. the primary aim of this approach is to have a thorough search
to gain right collections of objects and their respective configurations. But the
main point to see is that probabilistic approach is usually seen as better than
the present discussion of the topic. It offers a searching technique for the nonstatistical approaches whereas the probabilistic
method often deals with the uncertainties of the image.

 

Its main
characteristic is the optimization approach. The
non-probabilistic approach usually exhibits good properties having to
relate to other methods as well. Eve, today’s
real-time system is based on this non-probabilistic approach.

 

In one
approach, called as eigentracking, a set
of images represents an object entirely. There were other relative works as
well using the same model system.

 

 

 

 

 

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