[Đồ án] Surveillance Enhancement System - TG.PALLIYAGURU P.N.A
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Báo cáo cuối kỳ này trình bày thiết kế và triển khai Hệ thống Nâng cao Giám sát tích hợp Trí tuệ Nhân tạo (AI) và Thiết bị Internet of Things (IoT) để cải thiện an ninh đô thị, đạt độ chính xác phát hiện hoạt động đáng ngờ lên tới 97% và thời gian phản hồi nhanh chóng.
描述
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 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 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 realtime 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 realtime 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 mech
AI 摘要
- 文档名称
- [Đồ án] Surveillance Enhancement System - TG.PALLIYAGURU P.N.A
- 学校 / 课程
- Sri Lanka Institute of Information Technology · Artificial Intelligence
- 作者(文档中)
- PALLIYAGURU P.N.A
- 内容
- Nghiên cứu này giới thiệu một hệ thống giám sát tiên tiến sử dụng AI và IoT để tự động phát hiện các hoạt động đáng ngờ, nâng cao hiệu quả an ninh cho đô thị thông minh. Hệ thống đạt độ chính xác cao và thời gian phản hồi nhanh, có khả năng ứng dụng rộng rãi.
- 目录
- 5. ACKNOWLEDGEMENT
- LIST OF TABLES
- LIST OF FIGURES
- LIST OF ABBREVIATIONS
- 1. INTRODUCTION
- 1.1. Background
- 1.2. Literature Survey
- 1.3. Research Gap
- 1.4. Research Problem
- 1.5. Research Objectives
- 1.5.1. Main Objectives
- 1.5.2. Sub Objectives
- 2. METHODOLOGY
- 2.1. Materials and Methods
- 2.1.1. Problem Statement
- 2.1.2. Component System Architecture (Solution Design)
- Video Surveillance and Computer Vision Module
- Centralized Monitoring and Control Dashboard
- Automated Alert and Response Mechanism
- Data Storage and Cloud Integration
- System Scalability and Adaptability
- 2.1.3. Processing and Augmentation
- 2.2. Commercialization Aspects of The Product
- 2.3. Implementation
- 2.4. Testing
- 2.4.1. Test plan and strategy
- 2.4.2. Test Case Design
- 3. Results and Discussions
- 3.1. Results
- Alert Generation and Monitoring Dashboard Performance
- System Reliability and Real-World Applicability
- 3.2. Product Development
- 3.3. Research Findings
- 3.3.1. Model Architecture and Training
- 3.3.2. Performance Metrics
- 3.3.3. Robustness and Practical Application
- 3.3.4. Significance and Implications
- 3.4. Discussion
- 4. CONCLUSION
- 5. REFERENCES
- 6. APPENDICES
- 7. Table 1: Characteristics of Existing Research Literature and the Proposed System
- Table 3: Verify Waste Image Upload
- Table 4: Verify Waste Classification Prediction
- Table 5: Verify Automated Bin Lid Opening
- Table 6: Verify Bin Level Monitoring
- Table 7: Verify OLED Display Output
- Table 8: Verify Audio Output
- Figure 1: Projected waste generation, by region (millions of tones/year)
- Figure 2: Regional waste generation per person
- Figure 3: Proposed System for Smart Waste Management
- Figure 7: Overall System Diagram
- Figure 8: Smart Waste Management System
- Figure 9: Data Collection
- Figure 10: Hybrid Model Accuracy and Loss
- Figure 11: Analytics Dashboard Page
- Figure 12: Bin Analytics Page
- 页数
- 56 页
- 上传者
- Nguyen Le Giang
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[Đồ án] Surveillance Enhancement System - TG.PALLIYAGURU P.N.A
正在生成预览...
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 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 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 realtime 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 realtime 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 mech
阅读全文
- 文档名称
- [Đồ án] Surveillance Enhancement System - TG.PALLIYAGURU P.N.A
- 学校 / 课程
- Sri Lanka Institute of Information Technology · Artificial Intelligence
- 作者(文档中)
- PALLIYAGURU P.N.A
- 内容
- Nghiên cứu này giới thiệu một hệ thống giám sát tiên tiến sử dụng AI và IoT để tự động phát hiện các hoạt động đáng ngờ, nâng cao hiệu quả an ninh cho đô thị thông minh. Hệ thống đạt độ chính xác cao và thời gian phản hồi nhanh, có khả năng ứng dụng rộng rãi.
- 目录
- 5. ACKNOWLEDGEMENT
- LIST OF TABLES
- LIST OF FIGURES
- LIST OF ABBREVIATIONS
- 1. INTRODUCTION
- 1.1. Background
- 1.2. Literature Survey
- 1.3. Research Gap
- 1.4. Research Problem
- 1.5. Research Objectives
- 1.5.1. Main Objectives
- 1.5.2. Sub Objectives
- 2. METHODOLOGY
- 2.1. Materials and Methods
- 2.1.1. Problem Statement
- 2.1.2. Component System Architecture (Solution Design)
- Video Surveillance and Computer Vision Module
- Centralized Monitoring and Control Dashboard
- Automated Alert and Response Mechanism
- Data Storage and Cloud Integration
- System Scalability and Adaptability
- 2.1.3. Processing and Augmentation
- 2.2. Commercialization Aspects of The Product
- 2.3. Implementation
- 2.4. Testing
- 2.4.1. Test plan and strategy
- 2.4.2. Test Case Design
- 3. Results and Discussions
- 3.1. Results
- Alert Generation and Monitoring Dashboard Performance
- System Reliability and Real-World Applicability
- 3.2. Product Development
- 3.3. Research Findings
- 3.3.1. Model Architecture and Training
- 3.3.2. Performance Metrics
- 3.3.3. Robustness and Practical Application
- 3.3.4. Significance and Implications
- 3.4. Discussion
- 4. CONCLUSION
- 5. REFERENCES
- 6. APPENDICES
- 7. Table 1: Characteristics of Existing Research Literature and the Proposed System
- Table 3: Verify Waste Image Upload
- Table 4: Verify Waste Classification Prediction
- Table 5: Verify Automated Bin Lid Opening
- Table 6: Verify Bin Level Monitoring
- Table 7: Verify OLED Display Output
- Table 8: Verify Audio Output
- Figure 1: Projected waste generation, by region (millions of tones/year)
- Figure 2: Regional waste generation per person
- Figure 3: Proposed System for Smart Waste Management
- Figure 7: Overall System Diagram
- Figure 8: Smart Waste Management System
- Figure 9: Data Collection
- Figure 10: Hybrid Model Accuracy and Loss
- Figure 11: Analytics Dashboard Page
- Figure 12: Bin Analytics Page
- 页数
- 56 页
- 上传者
- Nguyen Le Giang
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