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Introduction

Edge AI for video analytics is an emerging technology that leverages artificial intelligence (AI) algorithms to analyze and process video data directly on edge devices, such as cameras and sensors. This approach eliminates the need to transmit large amounts of data to a centralized server for processing, improving real-time response and reducing network bandwidth requirements. The potential applications of Edge AI for video analytics are vast, including surveillance, smart city infrastructure, healthcare monitoring, and industrial automation.

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 technologies
2.2 Video analytics algorithms
2.3 Edge computing architecture
2.4 Applications of Edge AI for video analytics
2.5 Edge AI hardware platforms
2.6 Edge AI software frameworks
2.7 Challenges and opportunities in Edge AI
2.8 Case studies in Edge AI for video analytics
2.9 Integration with cloud-based AI systems
2.10 Future trends in Edge AI for video analytics

Chapter 3: System Design and Methodology
3.1 System requirements and constraints
3.2 Data collection and preprocessing
3.3 Edge AI model selection
3.4 Training and optimization
3.5 Real-time processing and inference
3.6 Performance evaluation metrics
3.7 Security and privacy considerations
3.8 Integration with existing video surveillance systems

Chapter 4: System Implementation
4.1 Hardware and software setup
4.2 Edge AI algorithm implementation
4.3 Integration with edge devices
4.4 Testing and validation
4.5 Performance optimization
4.6 Scalability and deployment considerations
4.7 User interface design
4.8 Documentation and maintenance

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Limitations and future work
5.4 Implications for industry and academia
5.5 Concluding remarks

Thesis Overview:

The use of Edge AI for video analytics has gained significant attention in recent years due to its potential to revolutionize various industries. This thesis aims to investigate the application of Edge AI for video analytics and develop a comprehensive understanding of its capabilities, challenges, and opportunities. The research will involve a thorough review of the existing literature on Edge AI technologies, video analytics algorithms, edge computing architecture, and applications in diverse domains.

The study will also involve the design and implementation of a system that leverages Edge AI for real-time video analysis on edge devices. Methodologies for data collection, model selection, training, and inference will be explored to optimize system performance and accuracy. The thesis will provide insights into the integration of Edge AI with existing video surveillance systems, addressing security, privacy, and scalability considerations.

The findings of this research will contribute to the advancement of Edge AI for video analytics and provide valuable insights for industry practitioners and researchers. The limitations of the study and suggestions for future work will be discussed to guide further research in this rapidly evolving field. The thesis will conclude with a summary of key findings, implications for industry and academia, and concluding remarks on the potential impact of Edge AI for video analytics.

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