AuraFind: an integrated smart recovery system combining artificial intelligence, CCTV evidence matching, and automated notification services
Keywords:
Computer Vision, CCTV Matching, Image Similarity Analysis, Smart Recovery Platform, Geospatial Intelligence.Abstract
The problem of lost and misplaced items in public areas, institutions and workplaces remains, and conventional lost and found methods are hamstrung by manual filing and sluggish recovery. In this work we introduce AuraFind, an intelligent recovery platform that integrates Artificial Intelligence (AI), computer vision, geographic intelligence and real-time communication to revolutionize lost-item management. AuraFind operates on a straightforward three-step process. Users input information and photographs of the object, then submit CCTV proof, and then obtain AI-generated similarity matches using a web-based interface. Computer vision methods analyze photographs uploaded by users and compare them to CCTV video, assigning confidence values to aid in recovery. Twilio may automatically send SMS messages to immediately notify users if matches surpass certain limits. AuraFind also has a geographic hotspot visualization, QR-based identity authentication, trust and reputation authentication methods, anonymous communication channels and an AI-powered assistant to aid users through the process, besides picture verification. This application is developed using React, Node.js, Express and the Cloud services. Its scalable client-server design allows for fast data processing and real time interactions. AuraFind brings together many intelligent technologies into one ecosystem, improving recovery accuracy, reducing human work and accelerating cooperation amongst stakeholders. This article describes a feasible technology-driven solution for lost-and-found management and lays the groundwork for future research in intelligent recovery systems and AI-powered public services.
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Copyright (c) 2026 M. Hiteshi, Avinash Seekoli, Srikanth Veldandi, K. Meenendranath Reddy

This work is licensed under a Creative Commons Attribution 4.0 International License.