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Table of Contents:

Chapter One: Introduction
1.1 Background of the study
1.2 Statement of the problem
1.3 Objectives of the study
1.4 Research questions
1.5 Significance of the study
1.6 Limitations of the study
1.7 Scope of the study

Chapter Two: Literature Review
2.1 Overview of Edge Computing
2.2 Video Analytics in Edge Computing
2.3 Applications of Edge Computing for Video Analytics
2.4 Challenges and Issues in Edge Computing for Video Analytics
2.5 Current trends in Edge Computing for Video Analytics

Chapter Three: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Sample selection
3.4 Data analysis techniques
3.5 Ethical considerations

Chapter Four: Discussion of Findings
4.1 Data analysis results
4.2 Interpretation of findings
4.3 Comparison with existing literature
4.4 Implications for practice
4.5 Recommendations for future research

Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Conclusion
5.3 Contributions to the field
5.4 Recommendations for practitioners
5.5 Suggestions for further research

Brief Overview on Edge Computing for Video Analytics:

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, reducing latency and bandwidth usage. This technology has gained significant attention in recent years due to the proliferation of Internet of Things (IoT) devices and the need for real-time data processing.

Video analytics is the process of analyzing and interpreting video data to extract useful information, such as object detection, tracking, and behavior recognition. By combining edge computing and video analytics, organizations can process video data closer to its source, enabling real-time insights and decision-making.

Edge computing for video analytics offers several advantages, including reduced latency, improved security and privacy, and cost savings on data transmission and storage. However, there are also challenges, such as limited computing resources at the edge, scalability issues, and data security concerns.

This final year project will focus on exploring the current trends, applications, challenges, and opportunities in edge computing for video analytics. By conducting a thorough literature review and research methodology, the study aims to provide insights and recommendations for practitioners and researchers in this emerging field.

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