Artificial Intelligence Based Surveillance Systems: A Survey, Challenges and Future Trends by IRJET Journal

Artificial Intelligence Based Surveillance Systems: A Survey, Challenges and Future Trends

Abdullah Al-Atwi¹, *, Fayez Alkhaibari¹, Khalid Al-Malki1, Yasser Al-Juhani1, Hasan Shtiewi1

1. College of Computing, Fahad Bin Sultan University, Saudi Arabia *(Corresponding Author)

Abstract –AI-basedsurveillancesystemsleveragecomputer vision, machine learning, and IoT sensors toenable real-time monitoring, intelligent analysis, and automated threat response, enhancing security and operational efficiency in smart cities and public spaces. However, their successful implementation hinges on addressing critical cybersecurity and privacy challenges, including secure development frameworksandethicalhandlingofsensitivedatalikelocation information and medical records. This work analyzes these dual imperatives, providing researchers with insights to develop more secure and privacy-preserving surveillance solutions, ultimately fostering trustworthy smart city ecosystems that balance technological advancement with fundamentalrights protection.

Key Words: Surveillance, Security, Privacy, Agents, Medical Records.

1. INTRODUCTION

AI-based surveillance systems use computer vision, machinelearning,andIoTsensorstoautomaticallymonitor, analyze,andrespondtoactivitiesinrealtime.Thesesystems enhance security, automate threat detection, and improve operationalefficiencyinsmartcities,businesses,andpublic spaces[1]

1.2 Framework of AI-Based Surveillance Systems

AtypicalAIsurveillancesystemfollowsapipelinethat consistsoffivestages[2],asdescribedbelow:

1. Data Acquisition

 Sources: CCTV cameras, drones, IoT sensors, facialrecognitionscanners,licenseplatereaders.

 DataTypes:Videofeeds,thermalimaging,audio, motiondetectionsignals.

2. Preprocessing

 Noisereduction(e.g.,stabilizingshakyfootage).

 Frame extraction (converting video into analyzableimages).

3. AI Processing & Analysis

 ObjectDetection(YOLO,FasterR-CNN).

 FacialRecognition(DeepFace,ArcFace).

 Behavioral Analysis (anomaly detection using LSTMnetworks).

4. Decision-Making

 Real-time alerts (e.g., gun detection, unauthorizedaccess).

 Automated responses (e.g., locking doors, notifyingauthorities).

5. Storage & Retrieval

 Cloud/edgestorageforforensicanalysis.

 Indexeddatabasesforfastsearch(e.g.,findinga suspectacrossmultiplecameras).

1.2 Key Components of AI-Based Surveillance Systems

In terms of components, an AI-based surveillance system consists of five components that are integrated together to achieve the goal, where the input of a given componentwillbeusedasoutputtothenextcomponent[2].

Table1summarizesthecomponents.

Table -1:KeyComponentsofAI-basedSurveillanceSystems.

Component Function

Cameras&Sensors Capturevisual/audiodata

EdgeDevices Processdatalocally(lowlatency)

AIModels Analyzedataforthreats

Cloud/Server Store&analyzebulkdata

UserInterface Monitor&controlthesystem

Eachcomponentusesdifferenttechnologiesenablingit toachieveitsfunction.Theyareasfollows:

1. Cameras & Sensors: IP cameras, LiDAR, thermal imaging.

2. Edge Devices: NVIDIA Jetson, Raspberry Pi + AI accelerators.

3. AIModels:YOLOv8,OpenPose,DeepSORT.

4. Cloud/Server:AWSRekognition,AzureAI.

5. User Interface: Dashboards (e.g., Milestone XProtect).

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1.3 Applications in Smart Cities

AI-basedsurveillancesystemsarewidelyusedinsmart citiestofacilitatedailyactivitiesrelatedtodetectmalicious actionsorforfacilitatingmonitoringgovernmentalprocess [3].Belowisalistofapplicationssupportedwithrealworld examples.

1. Public Safety

 Gun/weapondetectionincrowds.

 Missingpersontrackingviafacialrecognition.

2. Traffic Management

 Automaticlicenseplaterecognition(ALPR)for tolls/parking.

 Congestionanalysisusingvehicleflowtracking.

3. Retail & Fraud Prevention

 Shopliftingdetectionviaposeestimation.

 Queuemonitoringtooptimizestaffallocation.

4. Industrial Security

 Intrusiondetectioninrestrictedareas.

 Worker safety compliance (e.g., helmet detection).

5. Healthcare

 Falldetectionforelderlycare.

 Crowddensitymonitoringinhospitals.

2. Related Works: Systematic Review

ThissectionprovidesareviewrelatedtosomeAI-based surveillance systems. In general, AI-based surveillance systemsareclassifiedintofourcategories,assummarizedin

Table2.

Table -2:TaxonomyofsomeAI-basedSurveillanceSystems.

System Name Key Features Applications

SmartHome System Facerecognition,voice control,fireandgasleak detection,remote control

Hawk-Eye

ThreatDetector Real-timethreat detection,motion detection,multi-class classification

AI-Powered

SmartCamera Long-range communication,object andfacerecognition, securedata transmission

Smart-Watcher Real-timeobject detection,motion detection,mobileapp alerts

Authorsofwork[4]proposedanintelligentembedded video monitoring system for home surveillance using IoT and computer vision techniques. The system integrates a motiondetectionalgorithmwithvideorecordingandalert mechanisms. It employs Raspberry Pi as the central controllerinterfacedwithadigitalcamera,capturingvideo ataresolutionof640x480at24fps.Theirapproachincludes erosion and dilation processing to fine-tune motion detectionandreducenoise.Humandetectionisprioritized only after movement is initially sensed, which saves processing power and ensures important events are recorded. The system achieved around 80% accuracy in recognizinghumanforms,althoughperformancedecreased with multiple subjects. The implementation also supports cloudstorageandremoteaccess,withSMSandemailalerts enhancing its practical usability. The results indicate reliability and efficiency, making it suitable for real-time homemonitoringapplications,withpotentialforextension tooutdoororbordersurveillance

Authorsofwork[5]developedasmartIoT-basedmulticamera surveillancesystememphasizingaffordabilityand modular flexibility. Unlike proprietary systems with expensive SDKs, their solution uses open-source image processingmethodsandsupportsmultiplecamerainputs. The system allows users to customize video analytics modules,improvingadaptabilityacrossvarioussurveillance setups. Methodologically, user feedback was the primary formofevaluation.Ausabilitysurveywith10participants indicated high awareness of CCTV systems and general satisfactionwiththeadditionalon-screenvideodetails.90% of users found the system usable, and 95% regarded it as moreaffordablethanexistingcommercialoptions.Although someusersfoundadditionalfeaturesdistracting,theoption toaddnewmoduleswasgenerallywell-received.Thesystem provides a strong base for enhancing existing camera infrastructures,especiallyforindividualsororganizations withlimitedbudgets

Home automation, security,and convenience

Public surveillancein schools,malls, andhighsecurityareas

Homeandcar security, industrial monitoring

Securityfor smallto medium-scale premises

Authors of work [6] introduced an intelligent video surveillanceanalysisservicebuiltonaPlatform-as-a-Service (PaaS)architectureforlarge-scalecloud-baseddeployment. Theirsystem,partoftheCityEyesproject,integratesmore than25,000surveillancedevicesacrossametropolitanarea. Keycomponentsincludealicenseplaterecognitionengine, camera health monitoring, and network video recorder (NVR)/digital video recorder (DVR) integration. The methodology involves centralized data processing and storageinthecloud,enablingreal-timeanalyticsandrapid incidentdetection.Thesystem'seffectivenesswasvalidated through its application in actual criminal investigations, whereitreducedthemanpowerneededforreviewingvideo data.Resultsconfirmedimprovedusabilityand efficiency, suggesting that cloud-based services can significantly enhancetraditionalsurveillanceoperations.Theirapproach alsodemonstratesscalability,makingitacandidatemodel forfutureintelligentsurveillanceinfrastructures

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Authors of work [7] proposed a public surveillance system that combines anomaly detection and weapon detection using a deep learning approach. The system incorporatesLong-termRecurrentConvolutionalNetworks (LRCN) for identifying abnormal events (e.g., fights, explosions)andYOLOfordetectingvisibleweapons.Their methodemphasizestheimportanceoftemporalmodelingin surveillanceandachievesaround79%accuracyforanomaly detection. Furthermore, they use MongoDB GridFS for efficientvideostorageandimplementaGUIformonitoring. Theframeworksupportsbothcrowddensityestimationand weapon detection in real-time, making it suitable for deployment in smart cities. While the architecture is innovative, especially in combining LRCN with YOLO. The lower performance may stem from the diversity and complexity of anomalies as opposed to specific object detection.

Authors of work [8] proposed a layered software architecturetailoredforthird-generationmobiledistributed videosurveillance(MDSV)systems.Thearchitectureissplit into two tiers: the vigilant tier for low-resource mobile devicesandtheanalysttier forhigh-capacitysystemslike PCs. Each tier comprises specialized layers handling communication, sensing, alerting, and processing. A prototype was developed and tested, showing the architecturesupportskeysurveillancefunctionalitiessuch as protection, detection, and response while operating efficientlyeveninresource-constrainedenvironments.The architecture also provides developers with a structured guideline for building robust MDSV systems. Future plans includeincorporatingcomputervisionforactionandobject recognition

Authorsofwork[9]developedaninnovativeIoT-based surveillance system that integrates smart cameras with chatbotfunctionalitythroughplatformslikeTelegramand WhatsApp.Thesystememployscomputervisionalgorithms to continuously monitor environments, detect suspicious activities, and provide real-time alerts to users through conversational interfaces. This approach enables homeownersandsecuritypersonneltoremotelyaccesslive footage and receive instant notifications about potential threats.Thesolutionaddressesthelimitationsoftraditional surveillancesystemsbyofferingtwo-waycommunication, automatedthreatdetection,anduser-friendlyinteractionat areducedoperationalcost.Whileeffectivefordomesticand small-scale commercial applications, the authors suggest future enhancements could incorporate advanced facial recognition and deeper integration with smart home automationsystemstoexpanditscapabilities

Authors of work [10] developed an IoT-based smart doorbellsystemusingRaspberryPi,PIRmotionsensors,and a camera module to enhance home security. The system automatically detects visitors through motion sensing, capturestheirimages,andsendsreal-timenotificationswith photos to the homeowner's email and mobile device.

Experimental results demonstrated successful intruder detection,thoughtheaccuracydependedonpropersensor alignment and camera positioning. The researchers implemented the system using Python programming for GPIO connectivity and sensor integration, creating a costeffectivesolutionthatallowsremotemonitoringviainternet connectivity. While currently focused on basic motion detection and image capture, the authors suggest future enhancementscouldincorporatecomputervisionandAIfor advanced features like facial recognition and gesture identificationtofurtherimprovesecuritycapabilities

Authorsof work [11]createdanAI-powered weapon detectionsystemforsurveillancevideosusingTensorFlow framework, aiming to automate threat identification in public spaces. Their solution analyzes video footage to recognizefivecategoriesoffirearms(handguns,shotguns, automatic rifles, sniper rifles, and submachine guns) with precisionlevelsof0.8524(IoU=0.50)and0.7006(IoU=0.75). The system employs key frame extraction with optimal windowsizingtobalanceprocessingefficiencyanddetection accuracy, demonstrating improved performance through training iterations that reduced classification and localizationlosses.Whileeffectiveforweaponidentification, theresearchersacknowledgetheneedforexpandedtraining datasets and propose developing dual operation modes (energy-savingforlow-trafficareasandhigh-performance forcrowdedspaces)toenhancepracticalimplementationin varioussecurityscenarios.

3. Cybersecurity-Based Analysis

AI-basedsurveillancesystemscanbeanalyzedfromtwo mainaspects,whicharesecurityandprivacy.Theanalyzing leadstoextractchallengesandfuturetrends.

3.1 Security Based Analysis

AI-based surveillance systems use agent based technologyasaninfrastructuretodeveloprealsystemsthat contributetosolveproblems.Usingagents(bothstationary andmobileones)arisemanysecurityissues.Authorsof[12] presented a systematic review related to the security of agents. They presented security requirements, protection goals,andattacksthatmaybeoccurredandnegativelyaffect agents. The issues mentioned in their work should be considered during building agent based systems. in responding to such issues, authors of [13] presented a dummy based method to protect agents against host machineswhenactasanattacker.Insamecontext,authors of[14]proposedaselfprotectionapproachforagentsthat serve mobile cars in order to ensure secure information exchange. In regards to web security, collaboration based method is introduced in [15]. The key idea is to enable surveillance systems to take into consideration security issues arisen during collaboration in terms of data exchanging.Itisnoticedthatagentbasedsystemsdealwith big data within smart cities. Here, manipulating big data

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requiresspecialtechniquessuchasdatamining,textmining, and web mining [16]. Security issues related to such technologies should be considered to strengthen level of protection.

In conclusion, AI-based surveillance systems depend heavily on agent software technology, and many security issues open door to various vulnerabilities, security gaps, andthreats.SuchissuesmaynegativelyaffectsecurityofAIbasedsurveillancesystems.

3.2 Privacy Based Analysis

ManyAI-basedsurveillancesystemsuselocationbased services as primary way to facilitate daily activities of individualswithinsmartcities.Privacyissueisabigconcern thatmayleadtoseriousproblemsifprotectionofpersonal data isignored.In thecontextof privacyprotection, work [17]providedanapproachtoprotectKnnqueriesusedto monitorandretrievepoints ofintereststhatuserssearch. Fragmentation method is utilized to strengthen level of privacy so that attackers can mot obtain sensitive information about users. In the same context, work [18] introducedanAESbasedapproachthatharnessesdummies toprotectusers'privacyagainstservers.Suchapproaches aretakenfromasurveyprovidedin[19],whereresearchers detailed various privacy protection approaches and their related limitations. In terms of hiding within crowds, a leaderbasedmethodisproposedin[20].Thekeyideaisto mask activities against monitoring by exploiting random motionofmonitoredentities.Thisleadstostrengthenlevel ofprivacyinAI-basedsurveillancesystems,butstillsuffer form the fact that the leader can act as a malicious party even mutual benefit is used. Work [21] employed deep learningtechniquestodevelopprivacyprotectionsystem. CNN is used and can be used to strengthen AI-based surveillancesystems.However,privacyoftrainingdatamay beattacked. Work[22]addressedtheproblemofachieving load balancing between power consumption and privacy protection level. This is an important aspect in AI-based surveillance systems in terms of complexity and computationalresources.

Inmedicaldomain,AI-basedsurveillancesystemsplay important role, but privacy of medical data is critical. In terms of privacy, medical records of patients must not be revealed. AI-based surveillance systems developers must take responsibility related to protect patients' data when usingdeeplearningtechniques.Inwork[23]breastcancer diagnosticsystemisproposedusingenhancedconvolutional neural networks, where privacy is protected by removing IDsandnamesofpatients.Similarly,thework[24]proposed lungcancerdetectionsystem,andhereprivacyisensuredby maskingsensitiveinformationattachedwithmedicalimages. DuringCOVID-19epidemic,researchsocietymadearacefor developingdiagnosticintelligentsystemstodetectthisvirus anditsseries.Work[25]proposedmedicalsystemtodetect

covide-19 using advanced deep learning technique supported by an augmentation method. Applying augmentation on medical images may reveal personal information about training medical records. Privacy is ensured by using obfuscation technique that depends on replacingpersonalinformationbyarangeofdata.Thekey ideaisderivedfromasurveyprovidedinwork[26].

4. Challenges and Future Trends

Basedontheanalysingprovidedinprevioussections, cybersecuritybasedchallengescanbesummarizedintow pointsasfollows:

1. When developing AI-based surveillance systems using agents, security of agents must be ensured. Otherwise,thesystemwillcrashastheagentsare responsible for performing tasks. When tasks are modified,theresultswillbepoisoned.

2. When developing AI-based surveillance systems using location based services, privacy must be ensuredasattackersmaytracklocationsofusersor analysingtheissuedqueries.Otherwise,amalicious profilecanbecreatedaboutvictims(users).

3. WhendevelopingAI-basedsurveillancesystemsin medicaldomain,sensitivedataofpatientsmustbe protected. Otherwise, medical companies will undergopenaltiesaccordingtolaws.

Future trends in AI-based surveillance systems are inspirefromthechallengesmentionedabove,whichare:

1. Developingnovelcontrolstoprotectagents.

2. Developingnovelcontrolstoprotectprivacy.

5. CONCLUSION

SecurityandprivacyinAI-basedsurveillancesystems arecriticalforsuccessofsuchsysteminreality.Thiswork provided analysing of the tow aspects of cybersecurity. Analysing includes agents used to develop AI-based surveillancesystemsaswellasprivacywhenusinglocation based services and handling medical records. This work helpsresearcherstofocusofsecurityandprivacyaspectsin their future works. When both security and privacy are ensuredinAI-basedsurveillancesystems,smartcitieswill befoster.

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