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Introduction:

In recent years, the development of smart cities has gained momentum thanks to advancements in technology such as the Internet of Things (IoT) and artificial intelligence (AI). The integration of these technologies has led to the creation of innovative solutions for various urban challenges, including traffic congestion, energy efficiency, and public safety. One of the key components of smart cities is real-time video analytics, which involves processing and analyzing video data in real-time to extract valuable insights and make informed decisions. However, traditional cloud-based video analytics solutions face challenges such as latency, bandwidth constraints, and privacy concerns. Edge AI, which involves deploying AI algorithms directly on edge devices, offers a promising alternative to overcome these challenges and enable real-time video analytics in smart cities.

Table of Contents:

Chapter 1: Introduction

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review

2.1 Overview of Smart Cities
2.2 Real-Time Video Analytics
2.3 Edge Computing
2.4 Artificial Intelligence
2.5 Edge AI for Video Analytics
2.6 Applications of Edge AI in Smart Cities
2.7 Challenges and Opportunities
2.8 Case Studies
2.9 Current Trends and Future Directions
2.10 Gaps in Existing Literature

Chapter 3: Research Methodology

3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Edge AI Framework Development
3.5 Hardware and Software Requirements
3.6 Validation and Testing Procedures
3.7 Ethical Considerations
3.8 Limitations and Assumptions

Chapter 4: Discussion of Findings

4.1 Performance Evaluation of Edge AI Framework
4.2 Comparison with Cloud-Based Solutions
4.3 Impact on Real-Time Video Analytics in Smart Cities
4.4 User Feedback and Recommendations
4.5 Scalability and Cost Analysis
4.6 Security and Privacy Implications
4.7 Potential Integration with Other Technologies
4.8 Case Studies and Use Cases

Chapter 5: Conclusion and Summary

5.1 Summary of Key Findings
5.2 Contributions to Knowledge
5.3 Implications for Practice
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview:

Edge AI has emerged as a promising technology for enabling real-time video analytics in smart cities. This thesis aims to explore the potential of Edge AI in enhancing the efficiency and effectiveness of video analytics applications in urban environments. The introduction provides an overview of the research background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter two presents a comprehensive literature review on smart cities, real-time video analytics, edge computing, artificial intelligence, and the applications of Edge AI in smart cities. Chapter three outlines the research methodology, including research design, data collection methods, data analysis techniques, edge AI framework development, hardware and software requirements, validation and testing procedures, ethical considerations, limitations, and assumptions. Chapter four discusses the findings of the study, including the performance evaluation of the Edge AI framework, comparison with cloud-based solutions, impact on real-time video analytics in smart cities, user feedback, scalability and cost analysis, security and privacy implications, potential integration with other technologies, and case studies. Finally, chapter five presents the conclusion and summary of the project, highlighting key findings, contributions to knowledge, implications for practice, recommendations for future research, and a concluding statement on the significance of Edge AI for real-time video analytics in smart cities.

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