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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 484
Smart Surveillance System through Computer Vision
Neha Gupta1, M. Hammad Zaid2, Mohd Bilal3, Harshit Saxena4, Rahul Dabral5
1Assistant Professor of Computer Science and Engineering, MIT College, Uttar Pradesh, India
2Student of Computer Science and Engineering, MIT College, Uttar Pradesh, India
3Student of Computer Science and Engineering, MIT College, Uttar Pradesh, India
4Student of Computer Science and Engineering, MIT College, Uttar Pradesh, India
5Student of Computer Science and Engineering, MIT College, Uttar Pradesh, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract -In safety and security lie at the heart of thewealth
of nation. Every person wants to feel safe and secure from
pitfalls. But today security is tough in allviewofourdailylives.
This The usual surveillance system requires human
interaction, to work more the number of cameras in home
more human is required to monitor them. To deal with that
problem, we suggest a smart surveillance system which is a
python application made using high grew computer science
field, which is “computer vision”. The computer vision is a
branch of computer science in which we use digital data such
as images, video, or real time data to extract beneficial
information to deal with real time problems, for now in this
paper we are mostly highlights the features of smart
surveillance system that are object detection, facerecognition,
object tracking, and alert system.
Key Words: Object Detection, Face Recognition, Object
Tracking, Alert System
1. INTRODUCTION
Countless home and office security systems work on the
same concept of security such as securing entry points of
home or offices such as doors, windows etc. for security we
use, CCTV cameras, that might be an issue using this, due to
countless amount of cameras available in home or offices,
security guards seems to all over watching over it, also the
number of existing cameras exceeds the number of security
guards to monitor them, that would be costly. By using
computer vision tools with deep learning algorithms,wecan
detect weapons and other dangerous objects by using real
time footage as well as pictures and scenes from video, to
deal with the threat, so we can say that growth of computer
vision is beneficial for society.
Computer vision is fast growing especially in the realm of
homes or offices security technology.Itcontainsfeaturesthat
cannot be forgotten, faked,orlost,becausethroughcomputer
vision, we are able to scan CCTV information in real time, so
that security teams are alert when someone breaches the
security.
As we know that, face recognition is very good for
authentication, because face is a physiological trait that is
simplest to identify between two people, it is one of the
forensics technologies that is always being research and
developed.
Computer Vision is a field of artificial intelligence (AI) that
enables computers to drive meaningful information from
digital images, and other visual inputs. And act or make
recommendations based on that information. If AI enables
computers to think, computer vision enables them to see,
observe,andunderstand.Computervisionworksmuchasthe
human vision, except humans have head start. Human sight
has the advantage of lifetime of context to train how to tell
objects apart, how far away they are, whether they are
moving and whether there is something wrong in an image.
Computer vision trains machines to perform these functions
but it has to do it in much less time with cameras, data and
algorithms rather than retinas, optics, nerves and a visual
cortex. including detection, face recognition, tracking, alert
system, the project's objective is to develop a smart
surveillance system that incorporates computer vision to
facilitate security in homes and offices.
Other research papers focused on constructing a security
system that can recognize a face, wherethisstudyscansfaces
that have been entered into the database and then matches
the photos obtained by the webcam, if the image isnotmatch
in the database, an alert is generate with tracking feature. In
this project, wecreated a smart surveillancesystem.Itaidsin
the selection of a suitable approachfrommanyoptionsbased
on our application needs, as well as the resolution of existing
difficulties in real-time applications to some extent. In real-
timescenarios with multiple variablesandseamlesssettings,
we obtain an accurate system. This technology may be
described as an automated surveillance system. This
technique is intended to be abletocombattheftinhomesthat
are frequently abandoned by their owners.
This work is expected to make a substantialcontributiontoa
new field of research on the use of face recognition,
detection, tracking, and alert technology in surveillance
systems. As a result, the purpose of this study is to develop a
smart surveillance system to enhance security system and
grammar.
2. REQUIREMENTS
As it is a software-based project. It must run on some
hardware and operating system following are the
requirements to run this software.
 Windows/Linux/Mac any version of python 3.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 485
 Packages in Python.
 OpenCV.
 NumPy.
 Face Recognition.
 Visual Studio IDE.
In case of Hardware requirements, you don’t need much but
still some of the requirements such as
 Working PC.
 Flashlight/ LED if using this at night.
 Webcam with Drivers Installed.
3. METHODOLOGY
In our project we have established features. Below are the
different features which can be performed by using this
project.
1. Object Detection.
2. Object Tracking.
3. Face Recognition.
4. Alert System.
3.1 Object Detection
In this project our first task is to detect the instances of
objects ofa certain class within an imageorvideosandinreal
time. To perform object detection in real time, we use object
detection techniques. Object detection technique is a
computer technology related to computer vision, image
processing, and deep learning that deals with detecting the
instances of objects in an image or videos.
There are various techniquesoralgorithmstoperformobject
detection in real time such as R-CNN (Region- Based
Convolutional Neural Networks), Fast R-CNN (Fast Region
Based Convolutional Neural Networks),andYOLO(YouLook
Only Once). In this project we use the YOLO algorithm to
perform object detection in real time.
Fig -1: Object Detection
3.2 Object Detection
After detecting the instances of the various objects, our next
task in our project is to track the movement of an objects.
Object tracking usually involves the process of object
detection.
• Object detection, where the algorithm classifies and
detects the object by creating a bounding box around it.
• Assigning unique identification for each object (ID).
• Tracking the detected object as it moves through frames.
Fig -2: Object Tracking
3.3 Face Recognition
After detecting the instancesofthevariousobjects,ourtaskis
to recognize the faces of the personappearinacamerarange.
To verify the identity of the person. If the face of the personis
not match in the face recognition database, then alert is
generated by the system through SMS.
Fig -3: Face Recognition
3.4 Alert System
If an unknown person is detected by the system, then alert
will be generated through theSMS service. To generatealert,
we use Twilio python library which generates SMS. Twilio
messaging API is being usedgloballytosendandreceiveSMS.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 486
The intelligence tracking features enable user to check if the
message is delivered or not.
Fig -4: Alert System
4. SYSTEM DESIGN
For the system design, the design can be represented using
the Use-Case Diagram and flow chart Diagram.
4.1 Use Case Diagram
Fig -5: Use-Case Diagram
According to the use-case illustration above, when the
unknown person is detected by the camera through object
detection, face recognition feature, a system generates the
alert through SMS to inform security system.
4.2 Flow Chart
Fig -6: Working Flow Chart
The smart surveillance system is initially initiated and
configured, as shown in the flow chart diagram. After
initialization and configuration of the system camera of the
system is initiated, due to which detection, tracking, and face
recognition occurs, if detecting personisunknownthen,alert
is generated by the system through SMS.
5. RESULTS
Fig -7: Face Recognition
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 487
Fig -7: Face Recognition with Tracking
6. Future Scope
When smart surveillance systems (SSS) identifies when
someone or anything tries to breach a system or resources.
An alert is generated through SMS for detecting unknown
person. However, with future development, a drone can be
used to track the unknown person’s location of unknown
person by following the unknown person and provide the
location of the unknown person to the security system.
7. CONCLUSIONS
Extensive research is going on in the field of computer
vision. In this project we will complete a bit of work to carry
on the project. The motive is to detect instances of objects, if
persons detected then process the faces, and identify
whether these faces match in the face recognition database.
If not match, then generate alert through SMS. Is still
challenging to enhance the security, the system fails when
the camera is damaged or does not work properly.
8. REFERENCES
[1] Arun Hampapur, Lisa Brown, Jonathan Connell, Sharat
Pankanti, Andrew Senior and Yingli Tian. Smart
surveillance: - Applications, technologies and
implications. ResearchGate. January 2004.D.
[2] Dayana R, Suganyam M, Balaji P, Mohammad Thahir A,
Arunkumar P. Smart surveillance system using Deep
Learning. International journal of Recent Technology
and Engineering. May2020.
[3] Paul Viola, Michael Jones. Rapid Object Detection using
Boosted a cascade of simple features. Reserchgate. May
2004.
[4] Kelvin salton do prado. Face Recognition on
understanding LBPH Algorithm. Towards Data science.
Nov 2017K.

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Smart Surveillance System through Computer Vision

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 484 Smart Surveillance System through Computer Vision Neha Gupta1, M. Hammad Zaid2, Mohd Bilal3, Harshit Saxena4, Rahul Dabral5 1Assistant Professor of Computer Science and Engineering, MIT College, Uttar Pradesh, India 2Student of Computer Science and Engineering, MIT College, Uttar Pradesh, India 3Student of Computer Science and Engineering, MIT College, Uttar Pradesh, India 4Student of Computer Science and Engineering, MIT College, Uttar Pradesh, India 5Student of Computer Science and Engineering, MIT College, Uttar Pradesh, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract -In safety and security lie at the heart of thewealth of nation. Every person wants to feel safe and secure from pitfalls. But today security is tough in allviewofourdailylives. This The usual surveillance system requires human interaction, to work more the number of cameras in home more human is required to monitor them. To deal with that problem, we suggest a smart surveillance system which is a python application made using high grew computer science field, which is “computer vision”. The computer vision is a branch of computer science in which we use digital data such as images, video, or real time data to extract beneficial information to deal with real time problems, for now in this paper we are mostly highlights the features of smart surveillance system that are object detection, facerecognition, object tracking, and alert system. Key Words: Object Detection, Face Recognition, Object Tracking, Alert System 1. INTRODUCTION Countless home and office security systems work on the same concept of security such as securing entry points of home or offices such as doors, windows etc. for security we use, CCTV cameras, that might be an issue using this, due to countless amount of cameras available in home or offices, security guards seems to all over watching over it, also the number of existing cameras exceeds the number of security guards to monitor them, that would be costly. By using computer vision tools with deep learning algorithms,wecan detect weapons and other dangerous objects by using real time footage as well as pictures and scenes from video, to deal with the threat, so we can say that growth of computer vision is beneficial for society. Computer vision is fast growing especially in the realm of homes or offices security technology.Itcontainsfeaturesthat cannot be forgotten, faked,orlost,becausethroughcomputer vision, we are able to scan CCTV information in real time, so that security teams are alert when someone breaches the security. As we know that, face recognition is very good for authentication, because face is a physiological trait that is simplest to identify between two people, it is one of the forensics technologies that is always being research and developed. Computer Vision is a field of artificial intelligence (AI) that enables computers to drive meaningful information from digital images, and other visual inputs. And act or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe,andunderstand.Computervisionworksmuchasthe human vision, except humans have head start. Human sight has the advantage of lifetime of context to train how to tell objects apart, how far away they are, whether they are moving and whether there is something wrong in an image. Computer vision trains machines to perform these functions but it has to do it in much less time with cameras, data and algorithms rather than retinas, optics, nerves and a visual cortex. including detection, face recognition, tracking, alert system, the project's objective is to develop a smart surveillance system that incorporates computer vision to facilitate security in homes and offices. Other research papers focused on constructing a security system that can recognize a face, wherethisstudyscansfaces that have been entered into the database and then matches the photos obtained by the webcam, if the image isnotmatch in the database, an alert is generate with tracking feature. In this project, wecreated a smart surveillancesystem.Itaidsin the selection of a suitable approachfrommanyoptionsbased on our application needs, as well as the resolution of existing difficulties in real-time applications to some extent. In real- timescenarios with multiple variablesandseamlesssettings, we obtain an accurate system. This technology may be described as an automated surveillance system. This technique is intended to be abletocombattheftinhomesthat are frequently abandoned by their owners. This work is expected to make a substantialcontributiontoa new field of research on the use of face recognition, detection, tracking, and alert technology in surveillance systems. As a result, the purpose of this study is to develop a smart surveillance system to enhance security system and grammar. 2. REQUIREMENTS As it is a software-based project. It must run on some hardware and operating system following are the requirements to run this software.  Windows/Linux/Mac any version of python 3.
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 485  Packages in Python.  OpenCV.  NumPy.  Face Recognition.  Visual Studio IDE. In case of Hardware requirements, you don’t need much but still some of the requirements such as  Working PC.  Flashlight/ LED if using this at night.  Webcam with Drivers Installed. 3. METHODOLOGY In our project we have established features. Below are the different features which can be performed by using this project. 1. Object Detection. 2. Object Tracking. 3. Face Recognition. 4. Alert System. 3.1 Object Detection In this project our first task is to detect the instances of objects ofa certain class within an imageorvideosandinreal time. To perform object detection in real time, we use object detection techniques. Object detection technique is a computer technology related to computer vision, image processing, and deep learning that deals with detecting the instances of objects in an image or videos. There are various techniquesoralgorithmstoperformobject detection in real time such as R-CNN (Region- Based Convolutional Neural Networks), Fast R-CNN (Fast Region Based Convolutional Neural Networks),andYOLO(YouLook Only Once). In this project we use the YOLO algorithm to perform object detection in real time. Fig -1: Object Detection 3.2 Object Detection After detecting the instances of the various objects, our next task in our project is to track the movement of an objects. Object tracking usually involves the process of object detection. • Object detection, where the algorithm classifies and detects the object by creating a bounding box around it. • Assigning unique identification for each object (ID). • Tracking the detected object as it moves through frames. Fig -2: Object Tracking 3.3 Face Recognition After detecting the instancesofthevariousobjects,ourtaskis to recognize the faces of the personappearinacamerarange. To verify the identity of the person. If the face of the personis not match in the face recognition database, then alert is generated by the system through SMS. Fig -3: Face Recognition 3.4 Alert System If an unknown person is detected by the system, then alert will be generated through theSMS service. To generatealert, we use Twilio python library which generates SMS. Twilio messaging API is being usedgloballytosendandreceiveSMS.
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 486 The intelligence tracking features enable user to check if the message is delivered or not. Fig -4: Alert System 4. SYSTEM DESIGN For the system design, the design can be represented using the Use-Case Diagram and flow chart Diagram. 4.1 Use Case Diagram Fig -5: Use-Case Diagram According to the use-case illustration above, when the unknown person is detected by the camera through object detection, face recognition feature, a system generates the alert through SMS to inform security system. 4.2 Flow Chart Fig -6: Working Flow Chart The smart surveillance system is initially initiated and configured, as shown in the flow chart diagram. After initialization and configuration of the system camera of the system is initiated, due to which detection, tracking, and face recognition occurs, if detecting personisunknownthen,alert is generated by the system through SMS. 5. RESULTS Fig -7: Face Recognition
  • 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 487 Fig -7: Face Recognition with Tracking 6. Future Scope When smart surveillance systems (SSS) identifies when someone or anything tries to breach a system or resources. An alert is generated through SMS for detecting unknown person. However, with future development, a drone can be used to track the unknown person’s location of unknown person by following the unknown person and provide the location of the unknown person to the security system. 7. CONCLUSIONS Extensive research is going on in the field of computer vision. In this project we will complete a bit of work to carry on the project. The motive is to detect instances of objects, if persons detected then process the faces, and identify whether these faces match in the face recognition database. If not match, then generate alert through SMS. Is still challenging to enhance the security, the system fails when the camera is damaged or does not work properly. 8. REFERENCES [1] Arun Hampapur, Lisa Brown, Jonathan Connell, Sharat Pankanti, Andrew Senior and Yingli Tian. Smart surveillance: - Applications, technologies and implications. ResearchGate. January 2004.D. [2] Dayana R, Suganyam M, Balaji P, Mohammad Thahir A, Arunkumar P. Smart surveillance system using Deep Learning. International journal of Recent Technology and Engineering. May2020. [3] Paul Viola, Michael Jones. Rapid Object Detection using Boosted a cascade of simple features. Reserchgate. May 2004. [4] Kelvin salton do prado. Face Recognition on understanding LBPH Algorithm. Towards Data science. Nov 2017K.