International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024 www.irjet.net p-ISSN: 2395-0072
SMART CCTV USING OPENCV
Prof. Heena Patil1 , Komal Farde2 , Nisha Bangar3 , Tanishka Pawar4 , Vedant Gujrathi5
1Head of Department, Dept. of AIML Diploma, ARMIET, Maharashtra, India
2Student, Dept. of AIML Diploma, ARMIET, Maharashtra, India
3Student, Dept. of AIML Diploma, ARMIET, Maharashtra, India
4Student, Dept. of AIML Diploma, ARMIET, Maharashtra, India
5Student, Dept. of AIML Diploma, ARMIET, Maharashtra, India
Abstract -Bybringingstate-of-the-artcomputervisionand image processing techniques to traditional closed-circuit television (CCTV) systems, the Smart CCTV Project using OpenCV marks a revolutionary leap that will transform security surveillance. This project suggests an all-inclusive surveillance system with real-time object detection, tracking, and optional facial recognition features by utilizing OpenCV. Thetechnologyensuresaccurateobjectdetectionandtracking even in difficult conditions by improving frame quality through sophisticated algorithms and image enhancement techniques. Alerts that are set out when particular items or events are detectedallowfor quick reactions to security risks, which increases the efficacy of surveillance as a whole. An additional degree of protection is provided by the optional facial recognition capability, which makes it possible to identifyandfollowanyoneinsidethemonitoredarea.Through the use of OpenCV and cutting-edge technologies, the Smart CCTV Project seeks to enhance safety and security by redefining the capabilities of conventional CCTV systems.
Key words:-Smart cctv camera, Machine Learning, facial recognition, surveillance, security, computer vision, image processing, real-time monitoring.
1. INTRODUCTION
In the realm of security and surveillance, smart CCTV (closed-circuittelevision)systemshavebecomeacuttingedgeinnovation.Thesesystemshavecompletelychanged traditional video surveillance by utilizing the power of computer vision technology, especially OpenCV (Open SourceComputerVisionLibrary),whichallowsforreal-time analysis,objectdetection,andintelligentdecision-making. An open-source library called OpenCV offers a strong foundationforcreatingSmartCCTVappsthatcanidentify andreacttoparticulareventsorbehaviors.Theseappsarea pricelesstoolforimprovingsecurityandsurveillanceina variety of settings, including public areas, homes, and workplaces.Intheend,thistechnologyshouldincreasethe safetyandsecurityofoursurroundingsbyimprovingthe effectiveness of CCTV systems and providing a more proactive and responsive approach to security. This project offers a sophisticated CCTV model that is intendedtoactasaresponsiblesecurityguard.Themodel
identifypeople,identifynoise,andrecord.Whenthereisno signofthievery,itworksbytakingpicturesandkeepingan eyeoutofmovement.Itrecordstheeventwhenitdetects motion.Themodelalsohasaspecial"IdentifyMe"option that lets you train it on people you know to get better recognition.ThebenefitsofsmartCCTVtechnology,which combines machine learning and artificial intelligence to improveuponthefeaturesofconventionalCCTVsystems, arehighlightedintheintroduction.SmartCCTV,incontrast to traditional systems, has the ability to analyze data instantly, spot anomalies, and alert security staff as necessary.Itprovidesadvantageslikeimprovedprecision, increased effectiveness, decreased false alarms, and the flexibility to customize solutions to meet different organizational needs, making it a flexible and efficient optionforhandlingarangeof
1.1 Problem Statement:
The problem statement for a Smart CCTV project using OpenCVrevolvesaroundtheneedtoenhanceconventional closed-circuit television (CCTV) systems with advanced capabilities. Traditional CCTV systems often provide raw video footage that necessitates manual monitoring and interpretation,whichcanbetime-consumingandproneto errors.Theproblemliesintheinefficiencyofthesesystemsin detecting and responding to security threats, incidents, or specificeventsinreal-time.Toaddressthis,ourprojectseeks toleverageOpenCV,anopen-sourcecomputervisionlibrary, todevelopasmartCCTVsystemcapableofautomatedobject detection, tracking, and, optionally, facial recognition. The primarychallengeistodesignandimplementasystemthat canprocessvideostreams,analyzeframes,andintelligently identifyandtrackobjectsorindividualsofinterest,thereby improving the efficiency and effectiveness of surveillance, enhancing security, and reducing the burden of manual monitoring.Additionally,ensuringtheprivacyandethicaluse offacialrecognitiontechnologywillbeacrucialconsideration intacklingthisproblem
1.2 Purpose
The goal of an OpenCV-powered Smart CCTV system is to improvesecurityviaautomationandreal-timemonitoring.It increases security and generates alarms by detecting and tracking items. It can also discourage wrongdoers and hasmultipleproperties,suchastheabilitytodetecttheft,
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024 www.irjet.net p-ISSN: 2395-0072
provide data analysis for well-informed decision-making. Thesystem'sdocumentationfeaturefacilitatesinquiriesand raisesstandardsofopenness.Anadditionaldegreeofaccess restrictionisprovidedbyoptionalfacialrecognition.Italso lowerslaborexpensesandincreasesoperationalefficiency acrossarangeofsectors.Becauseofitsadaptability,OpenCV maybecustomizedtomeetparticularrequirements,making itastrongandflexibletoolforestablishingsettingsthatare saferandmoreeffective.
1.3 Scope
ThisprojecthasabroadscopetheOpenCV-poweredSmart CCTVsystemservesavarietyoffunctions.Itsmaingoalsare todecreasetheneedformanualmonitoringbyautomating object tracking and real-time threat detection, hence improving security. It uses automation, which increases productivity, lowers expenses, and makes useful data analysis possible. Visible security cameras serve as a warning to prospective offenders. Transparencyandproofareprovidedbythesystem'sability to record occurrences and incidents. An extra degree of securityisaddedviafacialrecognition,whichisoptionalfor access control. It improves operational efficiency across a rangeofbusinessesinadditiontosecurity
2. SYSTEM ARCHITECTURE
Modules used for this are:
Input Module Thismodulemanagestheinputsource,which is usually a stream from a camera. The video stream is capturedandsenttotheprocessingmodule.
Processing Module: Preprocessing the incoming video streamistheresponsibilityoftheprocessingmodule.This includesshrinkingframes,applyinganyrequiredfiltersto improveimagequality,andconvertingframestograyscale (forsomealgorithms).Additionally,itusestheHaarCascade classifierorothersophisticatedOpenCValgorithmstocarry out object detection and tracking. This module may optionally have facial recognition software to detect wellknownpeople.
Decision-Making Module: After detecting and tracking facesorobjects,thismoduleusespresetcriteriaorrulesto makedecisions.Forinstance,itmightsoundanalarmifit notices a specific object like a person lingering in a prohibitedarea orifarecognizedfacematchesoneona watchlist. Additionally, this module might be in charge of handling warnings, recording occurrences, and correspondingwithoutsidesystems(likesendingemailor SMSmessages).
Output Module: Theoutputmoduleshowstheprocessed videostreamalongwithanyalertsornotificationsthatthe
decision-making modulecreated,along with overlays that show any faces or objects that were discovered.A user interfaceforadjustingsystempreferences,examininglogs, andaccessingotherfunctionalitymightalsobeincluded.
Database: Thesystemmighthaveadatabasetohold event logs, facial recognition training data, configuration settings,andotherpertinentdata.
External Interfaces:The Smart CCTV system enable a coordinated reaction to security issues, the smart CCTV system may be integrated with external interfaces like security systems, access control systems, or other surveillancesystems.Theuserinterface,orUI
A graphical user interface (GUI) Userscaninteractwith thesystem,viewlivevideofeeds,adjustsettings,andaccess historicaldataandrecordsthroughaneasy-to-usegraphical userinterface(GUI).
Alerting System: An alerting system notifies security personnel or designated authorities in real-time when securitythreatsorsuspiciousactivitiesaredetected.
4. SYSTEM REQUIREMENTS
4.1 Software Requirements
Operating System:Anyoperatingsystemcompatiblewith OpenCV(e.g.,Windows,macOS,Linux)
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024 www.irjet.net p-ISSN: 2395-0072
Python: Version3.6orhigher
OpenCV: Version3.xor4.xinstalled(withPythonbindings)
Integrated Development Environment (IDE): Optionalbut recommendedfordevelopment(e.g.,PyCharm,VisualStudio Code,JupyterNotebook)
OpenCV: Open SourceComputerVisionLibrary forimage processing,objectdetection,andfacialrecognition Python:ProgramminglanguageusedforcodingtheSmart CCTVsystem
Haar Cascade Classifier: Pre-trained machine learning modelforfacedetection
VideoCapture:OpenCVfunctionforaccessingandcapturing videostreamsfromcameras
Cascade Classifier:OpenCVclassforobjectdetectionusing HaarCascadeclassifiers
4.2 Hardware Requirements
Camera: Awebcam or anyother compatiblecamera forcapturingvideoinput.
Computer: A computer capable of running OpenCV andprocessingreal-timevideostreams
Memory: Adequate RAM for video processing and objectdetectionalgorithms
4.3 Technology Used
OpenCV: Open Source Computer Vision Library for image processing, object detection, and facial recognition
Python: Programming language used for coding the SmartCCTVsyste.
Haar Cascade Classifier: Pre-trained machine learningmodelforfacedetection
VideoCapture: OpenCV function for accessing and capturingvideostreamsfromcameras.
Cascade Classifier: OpenCVclassforobjectdetection usingHaarCascadeclassifiers
6.PROJECT IMPLEMENTATION
A real-time face detection project can be built upon the providedcode,whichcanbemodifiedand expandedfora range of uses. This project works by continually taking picturesfromacamera,identifyingfacesineachframe,and then encircling those faces with green rectangles using OpenCV and a Haar Cascade classifier. A number of improvementsandfeaturescanbeaddedtomakethisintoa full-fledged project. For security reasons, it might be incorporatedintoa SmartCCTVsystem.Certainactivities, including sending alarms, capturing video, or even recognizingandidentifyingpeople,canbetriggeredbythe facesthatarespotted.Additionally,itcanbeutilizedinretail settingstotrackconsumeractivity,countpersonsenteringa building, and regulate attendance. Additionally, by accommodatingdifferentlightingsituationsandangles,the systemcanbestrengthenedevenfurther.Moresophisticated face identification models, notably those based on deep learning techniques like MTCNN or Single Shot MultiBox Detector(SSD),canbeusedtodothis.Moreover,youmay usefacialrecognitionsoftwaretoverifypeople,openingthe doortofeatureslikecustomizedservicesandaccesscontrol. The present project exhibits potential as a flexible instrument for several fields such as retail, security, and attendance management. It may be customized and enhancedtofulfillcertaindemands.
OUTPUT
1.MOTION
DETECTION
theprogramwhenArtificialintelligencestartrecognizingthe motiondetectioncamera.Motiondetectionisatechnology and process used in various fields to identify and track movementsorchangesinanenvironmentorasequenceof images or frames. It typically involves analyzing video frames or image sequences to determine if there is any motionoccurringwithinaspecifiedregionofinterest
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024 www.irjet.net p-ISSN: 2395-0072
2. FACE DETECTION
Theprogramshowsfacedetection.FacedetectioninaSmart CCTV (Closed-Circuit Television) system refers to the process of automatically identifying and locating human faceswithinthevideofeedcapturedbysurveillancecameras
7.CONCLUSION
InthisprojectwehavemadetheSmartCCTVsystemsusing OpenCVrepresentasignificantleapforwardintherealmof surveillanceandsecurity.Thesesystemsleverageadvanced computervisioncapabilitiestoenhancesecuritymeasures, automate monitoring, and offer valuable insights through dataanalysis.Byprovidingreal-timethreatdetection,object tracking,andpromptincidentresponses,theycontributeto saferenvironmentsandmoreefficientresourceutilization. WhiletheadvantagesofSmartCCTVareevident,thereare also challenges to overcome, including privacy concerns, potential false alarms, and implementation costs. Facial recognitiontechnology,thoughpowerful,introducesethical and bias concerns, requiring careful consideration and compliancewithlegalregulations.Ultimately,thesuccessof Smart CCTV systems using OpenCV hinges on a balanced approachthatprioritizessecuritywhilerespectingprivacy andaddressingtheevolvingethicallandscape.Astechnology continuestoadvance,thesesystemswillplayanincreasingly vital roleinsecurity and efficiencyacross varioussectors, offeringacustomizableandpowerfulsolutionforaneverchangingworld.
8.REFERENCES
[1] Michael F.Adaramola ,Michael.A.K.Adelabu “ImplementationofClosed-circuitTelevision(CCTV)Using Wireless Internet Protocol (IP) Camera” 1.School of Engineering,LagosStatePolytechnic,Ikorodu,P.M.B.21,606, Ikeja.Lagos.Nigeria
[2]TanZhang,AakankshaChowdhery,ParamvirBahl,Kyle Jamieson,SumanBanerjee“TheDesignandImplementation of a Wireless Video Surveillance System” University of Wisconsin-Madison, Microsoft Research Redmond, UniversityCollegeLondon
[3] C M Srilakshmi1, Dr M C Padma2 “IOT BASED SMART SURVEILLANCESYSTEM”InternationalResearchJournalof EngineeringandTechnology(IRJET)Volume:04Issue:05| May-2017
[4]Mrs.PrajaktaJadhav1,Mrs.ShwetaSuryawanshi2,Mr. Devendra Jadhav3 “Automated Video Surveillance eISSN: 2395 -0056 | p-ISSN: 2395-0072” International Research JournalofEngineeringandTechnology(IRJET)Volume:04 Issue:05|May-2017
[5] Pawan Kumar Mishra “A study on video surveillance system for object detection and tracking” Nalina.P, Muthukannan.K
[6]JianLiang“Camera-BasedDocumentImageMosaicing” 18th International Conference on Pattern Recognition (ICPR'06)
[7] Akanksha Rastogi, Abhishesh Pal, Beom Sahng Ryuh “Real-TimeTeatDetectionusingHaarCascadeClassifierin Smart Automatic Milking System” 2017 7th IEEE InternationalConferenceonControlSystem,Computingand Engineering,24–26November2017,Penang,Malaysia [8] PaulViola,MichaelJones