Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chaurasiya, Hirendra Kumar; Kumari, Muskan; Sharma, Vandana; Singh, Ajay Vikram; Jain, Sarika Title A name given to the resource Design of a Multi Camera Enabled Scrutinizing Framework for Smart Cities Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Artificial Intelligence and Machine Vision, AIMV 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AIMV66517.2025.11203693" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIMV66517.2025.11203693</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021802275?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021802275?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chaurasiya H.K., Kalinga Institute of Industrial Technology, School of Computer Engineereing, Bhubaneshwar, India; Kumari M., Kalinga Institute of Industrial Technology, School of Computer Engineereing, Bhubaneshwar, India; Sharma V., CHRIST University, Computer Science Department, Bengaluru, India; Singh A.V., Amity University, AIIT, UP, India; Jain S., Amity University Uttar Pradesh, AIIT, Noida, India Description An account of the resource This research paper presents a multi-camera surveillance system tailored for the demands of smart cities. By integrating edge computing, the system decentralizes processing, reducing latency and alleviating network bandwidth strain. The architecture includes layers for data collection, edge processing, centralized storage, AI-driven analysis, synchronization, and user visualization. Cameras capture and preprocess data locally to identify anomalies and minimize unnecessary transmission. AI algorithms handle tasks like object tracking, behavior analysis, and event detection with precision. Synchronization ensures seamless temporal alignment across video streams for accurate event reconstruction. User-friendly dashboards provide actionable insights for urban planning and public safety. By leveraging edge computing, AI, and robust synchronization, this system addresses scalability, latency, and privacy concerns, offering enhanced safety, optimized traffic flow, and better urban planning. 2025 IEEE. Subject The topic of the resource AI-Driven Analysis; Edge Computing; Multi-Camera Systems; Real-Time Data Processing; Smart City Surveillance Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152697-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online