Real-Time Suspicious Behavior in Public Spaces (Theft Detection) (KEY IJP************040)

• Gaurav Kumar Lakhera

Abstract

AI and AR have revolutionized thefield of theft detection and suspicious behaviorrecognitioninpublicspaces,offeringunprecedented precision and situational awareness.Beyond traditional surveillance benefits such as247 monitoring and deterrence, these technologiesenable real-time decision-making by analyzingcomplex behavioral patterns in large-scale videodata. AI systems can automatically detectanomalies, classify suspicious activities, and trackindividuals across camera networks, ensuringenhanced security. Simultaneously, AR technologyoverlays dynamic visual cues onto live surveillancefeeds, helping security personnel visualize threats,predict movement patterns, and respond effectivelyto critical scenarios like theft or aggressivebehavior.Despite significant advancements, challengesremain, including false positives, data privacyconcerns, and computational resource demands.This paper presents innovative AI-driven detectiontechniques and AR-based visualization systemsthat integrate seamlessly to address these issues.By employing deep learning models for behaviorclassification and advanced AR displays forintuitive interaction, the proposed approachguarantees higher accuracy and operationalefficiency in theft prevention systems.This study provides a comprehensive overview ofmethodologies, results, and the potential future ofAI and AR in reshaping public safety measures,paving the way for intelligent and proactivesurveillance systems capable of revolutionizingtheft detection practices in diverse environments.Keywords: AI, Augmented Reality (AR), SuspiciousBehavior Detection, Theft Prevention, Real-TimeAnalysis, Deep Learning, Public Safety Systems.