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**Introduction**

Edge AI has emerged as a promising technology for enhancing smart surveillance systems by enabling real-time data processing and analysis at the edge of the network. This thesis focuses on exploring the potential of Edge AI for improving the efficiency and effectiveness of surveillance systems, particularly in terms of object detection, tracking, and recognition. By leveraging the computational power of edge devices, such as cameras and sensors, Edge AI can enable intelligent decision-making and automate surveillance tasks, thereby reducing the burden on human operators and enhancing overall security measures.

**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 Edge AI in Surveillance Systems
2.2 Object Detection Techniques in Surveillance
2.3 Object Tracking Methods
2.4 Face Recognition Algorithms
2.5 Edge Computing in Smart Surveillance
2.6 Privacy and Security Concerns in Surveillance Systems
2.7 Edge AI Applications in Public Safety
2.8 Real-world Case Studies
2.9 Challenges and Opportunities
2.10 Future Trends

**Chapter 3: Research Methodology**
3.1 Research Design
3.2 Data Collection
3.3 Data Analysis
3.4 Hardware and Software Requirements
3.5 Experimental Setup
3.6 Evaluation Metrics
3.7 Validation Methods
3.8 Ethical Considerations

**Chapter 4: Discussion of Findings**
4.1 Object Detection Performance
4.2 Tracking Accuracy
4.3 Recognition Rate
4.4 Computational Efficiency
4.5 System Scalability
4.6 User Interface Design
4.7 Integration with Cloud Services
4.8 Comparison with Traditional Surveillance Systems

**Chapter 5: Conclusion and Summary**
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Future Research Directions
5.5 Conclusion

**Thesis Overview (2000 words)**
Edge AI has revolutionized the field of smart surveillance systems by bringing intelligence to the edge of the network, where data is generated and processed in real-time. This thesis aims to explore the potential of Edge AI in enhancing surveillance tasks such as object detection, tracking, and recognition, with the ultimate goal of improving security measures and reducing human intervention. The literature review will provide an overview of existing research on Edge AI applications in surveillance, including object detection techniques, tracking methods, and face recognition algorithms. The research methodology section will outline the design, data collection, and analysis techniques used in the study, along with the hardware and software requirements for implementing Edge AI algorithms. The discussion of findings will present an in-depth analysis of the performance metrics, such as object detection accuracy, tracking efficiency, and recognition rate, to evaluate the effectiveness of Edge AI in surveillance applications. The conclusion and summary will highlight the key findings of the study, discuss the implications for the field, and suggest future research directions to further advance the use of Edge AI in smart surveillance systems.

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