IRJET- Monument Informatica Application using AR

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6939
Monument Informatica Application Using AR
Thube Bhavana1, Bora Tejashri2, Tanpure Supriya3, Prof.Bhosale Sachin4
1,2,3,4Dept. of Computer Engineering, Jaihind college,sppu, Maharastra, india
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Abstract - Tourism has become an important sector having
an impact on development of country’s economy. For many
locales, it is the most important source of welfare. So, in order
to guide tourists, there exist many types oftourguideschemes.
Among them, traditional guides, paper and mobile based
systems are most commonlyusedforprovidingtourroutesand
heritage information for tourists. In the above system tourist
needs to visualize what the guide wants to convey about the
ancient period or to read the informationofthemonument. By
considering the limitations of above methods, we are
proposing an Augmented RealitybasedApplication, which will
give tourists an interactive experience by superimposing an
informative video, text, images onto the captured view of the
monument. This application provides the location of nearby
monuments and have ability to identify the monuments
positioned close to the user position.Rating is provided in
application. The proposed system will be applicable to
educational and entertainment industries also.
Key Words: Augmented Reality, Computer vision, and
template matching algorithm, Multimedia Information
System– Augmented Realities, Image Processing.
1.INTRODUCTION
Augmented reality (AR) is a mixed reality where a virtual or
digital object is injected into real world. Augmentedrealityis
related to two terms: mixed reality and computer-mediated
reality. Basically uses camera and sensor technology of
existing hardware to capture the real world and then uses
specialized software to layover an interactive digital
element. This paper is based on Augmented Reality
technology in which the tourist will be able to see the
information as a video, text, images when user points onto
the front view of the monument. This will give tourist
flexibility to understand the history of the monument with
the help of the text, image and video superimposed onto the
frame containing that monument. This application provides
the location of nearby monuments. This AR based
application will provide tourist with varieties of languages
like English and Hindi. The images, textwill bedisplayed and
video will be played in the specific language that the tourist
understandswhichmakestheapplicationmoreuser-friendly
and interactive. Rating is provided in application.
2. RELETED WORK
2.1 Problem Statement
There has been lot of problems regarding visit historical
places, picnic points, temples etc. and has no knowledge
regarding that place that’s why peoples can hire guide and
pay maximum amount to those persons. Hence we
implement an Augmented Reality basedapplicationthat will
provide tourists, insights of the monument by
superimposing videos on its captured view. Alsoprovidethe
location of nearby turist places.
3. Template matching Algorithm
Some data mining pre-processing techniques extract
feature points such as image, text and video and then used
as input data to application. Various approacheshavebeen
proposed to extract these monument points from the
object. The basic approaches are as follows.
3.1.K-nearest neighbours KNN algorithm:
Here is step by step on how to compute K-nearest
neighbours KNN algorithm:
1. Determine parameter K = number of nearest neighbours
2. Calculate the distance between the query-instance and
all the training samples
3. Sort the distance and determine nearest neighbours
based on the K-th minimum distance
4. Gather the category Y of the nearest neighbours
5. Use simple majority of the category of nearest
neighbours as the prediction value of the query instance
Time complexity and optimality of kNN
kNN with preprocessing of training set
Training

Testing
kNN without preprocessing of training set
training
Testing
Training and test times for kNN classification is the
average size of the vocabulary of documents in the
collection.
3.2. Template Based Technique:
In machine learning we are often interested in selecting the
best hypothesis (h) given data (d). In a classification
problem, our hypothesis (h) may be the class to assign for a
new data instance (d).
One of the easiest ways of selecting the most probable
hypothesis given the data that we have that we can use as
our prior knowledge about the problem Bayes’ Theorem
provides a way that we can calculate the probability of a
hypothesis given our prior knowledge.
Bayes’ Theorem is stated as:
P(h|d) = (P(d|h) * P(h)) / P(d)
Where
 P(h|d) is the probability of hypothesis h
given the data d. This is called the
posterior probability.
 P(d|h) is the probability of data d given
that the hypothesis h was true.
 P(h) is the probability of hypothesis h
being true (regardless of the data).Thisis
called the prior probability of h.
 P(d) is the probability of the data
(regardless of the hypothesis).
You can see that we are interested in calculating the
posterior probability of P(h|d) from the prior probability
p(h) with P(D) and P(d|h).
After calculating the posterior probability for a number of
different hypotheses, you can select the hypothesis with the
highest probability. This is the maximum probable
hypothesis and may formally be called the maximum a
posteriori (MAP) hypothesis.
This can be written as:
MAP(h) = max(P(h|d))
or
MAP(h) = max((P(d|h) * P(h)) / P(d))
or
MAP(h) = max(P(d|h) * P(h))
The P(d) is a normalizing term which allows us to calculate
the probability. We can drop it when we areinterestedinthe
most probable hypothesis as it is constant and only used to
normalize.
Back to classification, if we have an even number of
instances in each class in our training data, then the
probability of each class (e.g. P(h)) will be equal. Again, this
would be a constant term in our equation and we could drop
it so that we end up with:
MAP(h) = max(P(d|h))
This is a useful exercise, because when readingupfurtheron
Naive Bayes you may see all of these forms of the theorem.
4. Proposed Work
Nowadays Tourism has become an important sector having
an impact on development of country’s economy and most
important source of welfare. So, Traditional guides, paper
and mobile based systems are most commonly used for
providing tour routes and heritage information for tourists.
Hence, to overcome the limitations of traditional methods
such as tourist needs to visualize what the guide wants to
convey about the ancient period or to read the information
of the monument, so a need arises for interactive guide
system that will help the tourist to understand and get
involved in the history of related monument. As the usage
smart phones are increasing in large extent, system aims to
develop an application for smart devices which will provide

Augmented Reality based application, which will give
tourists an interactive experience by superimposing an
informative video ,text ,images onto the capturedviewof the
monument.
System will allow user to get the authenticated information
about the monument through a video,textandimageformat.
System will contain informationabout3monumentsnamely
’Taj Mahal’ etc. and user will be able to see video in desired
languages eg English and Hindi. System will augment all the
information in form of a video about the monument on the
screen so user can interact with virtual information along
with actual monument.
Augmenting Monument Information:
i. User will be able to get the monumentinformation
by using videos, images, textual information of that
monument.
ii. The system will show a video of the history,
construction of the monument and information
about the people related to the monument.
5. Mathematical Model
Let S be the Whole system which consists:
S= {IP, U, I, D, ip}.
Where,
IP is the input of the system.
Pro is the procedure applied to the system to process the
given input.
OP is the output of the system.
1. Registration:
U={U1, U2, U3, ……Un }
U Is the user set that register in our system.
I= { I1, I2, I3, I4,…. In}
I is the set of image that stored in to dataset.
ip= { ip1, ip2, ip3,…. ipn}
ip is the image processing for user given image.
Input: { image}
Output:{ information }
6. System Architecture
Fig 1: Working of System
The Architecture Model applied to this project contains
two main blocks:
User:
– Click View (The System will be an AR based application
where the user needs to stand in front of the monument
covering the whole view of it. The system will then identify
which monument is onto the captured frame. After
identifying the valid monument the user need to choose
which language the user wants the information and finally
the user will be able to see the information in an interactive
way e.g. Video, Text, Images)
Admin/System: System Flow Outline:
– Video capturing module: Splits the video into frames.
– Image Transformation module: Transform the image
into gray-scale.
– Marker identification module: Identify the marker on the
transformed image.
– Marker Retrieval and Comparison module: Match the
Identified marker in the Data Set.

– Information retrieval module: Extract the video, text,
Images corresponding to the identified marker.
– Information rendering module: Augment the video, text,
Images over the marker
7. Result
.

8. CONCLUSIONS
The system architecture focuses on presenting
information of monuments to tourists in an interactive
way with the help of Augmented Reality. Using this
system the user will be able to get information about
the monument, its history and culture in an visualway,
improving user experience. System provides rating
function for user’s feedback. This system will provide
an authentic replacement to a tourist guide providing
information using text, images, videos depicting
ancient culture of the monument. Location of nearby
monument is also provided by system.
ACKNOWLEDGEMENT
Authors want to acknowledge Principal, Head of
department and guide for all the support and help
rendered. To express profound feeling of appreciation
to their regarded guardians for giving the motivation
required to the finishing of paper.

REFERENCES
[1] Marco Cavallo, Geoffrey Alan Rhodes, Angus Graeme
Forbes, ”Riverwalk: IncorporatingHistorical Photographs in
Public Outdoor Augmented Reality Experiences” 2016 IEEE
International Symposium on Mixed and Augmented Reality.
[2] N. Jayanthi, S. Indu,”Comparison of Image Matching
Techniques” 2017 International Journal of Latest Trends in
Engineering and Technology.
[3] Akil H. Sayyad , Santosh A. Shinde ,”Augmented Reality
Based Mobile Tour Guide System ” 2016 International
Research Journal of Engineering and Technology (IRJET).
[4] Alexandru Gherghina, Alexandru-Corneliu Olteanu and
Nicolae Tapus ,”A Marker-Based Augmented Reality System
for Mobile Devices” 2013 IEEE 4th
Experiment@International Conference.
[5]Gary Bradski, Adrian Kaehler,”Learning
OpenCV:Computer Vision withtheOpenCVLibrary”,O’Reilly
Media.

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