[Đồ án] Surveillance Enhancement System - TG.PALLIYAGURU P.N.A

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IT4010 Research Project 4th Year, 1st Semester Final Report IT21187278 PALLIYAGURU P.N.A B.Sc. (Hons) Degree in Information Technology Sri Lanka Institute of Information Technology April 2025

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DECLARATION We declare that this is our own work, and this proposal does not incorporate without acknowledgement any material previously submitted for a degree or diploma in any other university or Institute of higher learning and to the best of our knowledge and belief it does not contain any material previously published or written by another person except where the acknowledgement is made in the text. Name Student ID Signature Palliyaguru K.L.D.D.S IT21187278 The above candidate is carrying out research for the undergraduate Dissertation under our supervision. ……………………………………. …………………………………….10/04/2025 Ms. Wishalya Tissera Date (Supervisor) 2

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ABSTRACT In the modern era of smart city development, ensuring public safety and enhancing urban security have become critical challenges due to rapid urbanization, increasing population density, and rising security threats. Traditional surveillance systems, which primarily rely on manual monitoring through CCTV cameras, are often limited in their ability to provide real- time threat detection, continuous monitoring, and intelligent response mechanisms. This research presents the design and implementation of an advanced Surveillance Enhancement System that integrates Artificial Intelligence (AI), Internet of Things (IoT) devices, and real- time monitoring technologies to overcome the limitations of conventional security systems. The proposed system focuses on automating the detection of suspicious activities such as unauthorized access, loitering, object tampering, and abnormal human behaviors through the application of AI-based video analytics and IoT sensor integration. The system architecture includes high-definition surveillance cameras, motion and vibration sensors, acoustic detectors, automated alert generation mechanisms, and a centralized monitoring dashboard for real-time visualization and incident management. The results of the research indicate that the developed Surveillance Enhancement System achieved an impressive activity detection accuracy of approximately 97%, with fast response times of 1-2 seconds for alert generation after detecting suspicious events. The system's real- time monitoring capability, automated alert mechanism, and centralized data visualization significantly improved the efficiency of security management within smart environments. This research demonstrates the practical applicability, scalability, and reliability of the proposed system for use in various smart city sectors, including smart waste management zones, parking areas, public transport terminals, and sensitive infrastructure points. The Surveillance Enhancement System contributes significantly to the vision of smart cities by promoting automated security solutions, improving incident response time, and ensuring public safety. Future improvements can focus on integrating advanced technologies such as facial recognition, predictive analytics, and cloud-based storage for large-scale deployments, while addressing privacy and data security considerations. 3

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ACKNOWLEDGEMENT First and foremost, I would like to express my sincere gratitude to my supervisor Ms. Wishalya Tissera for the constant guidance and support which always helped me with the successful completion of my undergraduate research. Besides my supervisor, my sincere thanks also go to Miss Hansi De Silva, the co-supervisor of this research project, for being willing to help whenever it was needed. 4

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Table of Content DECLARATION...................................................................................................................2 ABSTRACT...........................................................................................................................3 In the modern era of smart city development, ensuring public safety and enhancing urban security have become critical challenges due to rapid urbanization, increasing population density, and rising security threats. Traditional surveillance systems, which primarily rely on manual monitoring through CCTV cameras, are often limited in their ability to provide real-time threat detection, continuous monitoring, and intelligent response mechanisms. This research presents the design and implementation of an advanced Surveillance Enhancement System that integrates Artificial Intelligence (AI), Internet of Things (IoT) devices, and real-time monitoring technologies to overcome the limitations of conventional security systems.....................................................................................................................3 The proposed system focuses on automating the detection of suspicious activities such as unauthorized access, loitering, object tampering, and abnormal human behaviors through the application of AI-based video analytics and IoT sensor integration. The system architecture includes high-definition surveillance cameras, motion and vibration sensors, acoustic detectors, automated alert generation mechanisms, and a centralized monitoring dashboard for real-time visualization and incident management. .........................................3 The results of the research indicate that the developed Surveillance Enhancement System achieved an impressive activity detection accuracy of approximately 97%, with fast response times of 1-2 seconds for alert generation after detecting suspicious events. The system's real-time monitoring capability, automated alert mechanism, and centralized data visualization significantly improved the efficiency of security management within smart environments..........................................................................................................................3 This research demonstrates the practical applicability, scalability, and reliability of the proposed system for use in various smart city sectors, including smart waste management zones, parking areas, public transport terminals, and sensitive infrastructure points. The Surveillance Enhancement System contributes significantly to the vision of smart cities by promoting automated security solutions, improving incident response time, and ensuring public safety. Future improvements can focus on integrating advanced technologies such as facial recognition, predictive analytics, and cloud-based storage for large-scale deployments, while addressing privacy and data security considerations.............................3 5

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