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Introduction

Edge computing is a paradigm that brings computation and data storage closer to the source of data generation, enabling real-time processing and analysis. In recent years, the increasing popularity of real-time video analytics for applications such as surveillance, smart cities, and industrial automation has driven the need for efficient edge computing solutions. Real-time video analytics involves the processing of high-definition video streams in real-time to extract valuable insights and make timely decisions.

This thesis focuses on exploring the use of edge computing for real-time video analytics, aiming to improve the efficiency and performance of video processing tasks. The study will investigate the design, implementation, and evaluation of an edge computing system for real-time video analytics, addressing the challenges and limitations faced in traditional centralized cloud-based approaches.

Chapter One: 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 Two: Literature Review
2.1 Overview of Edge Computing
2.2 Real-time Video Analytics
2.3 Edge Computing in Real-time Video Analytics
2.4 Challenges in Real-time Video Analytics
2.5 Edge Computing Architectures
2.6 Edge Devices and Sensors
2.7 Machine Learning Algorithms for Video Analytics
2.8 Performance Metrics for Video Analytics
2.9 Edge Computing Platforms
2.10 Edge Computing Security

Chapter Three: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Real-time Video Stream Processing
3.4 Machine Learning Model Selection
3.5 Model Training and Inference
3.6 Performance Evaluation Metrics
3.7 System Integration and Deployment
3.8 Edge Computing Resource Management

Chapter Four: System Implementation
4.1 Edge Computing Hardware Setup
4.2 Software Stack Configuration
4.3 Real-time Video Stream Processing Module
4.4 Machine Learning Model Integration
4.5 System Performance Optimization
4.6 Edge Device Communication Protocol
4.7 Integration with Cloud Services
4.8 Security Measures

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Conclusion

Thesis Overview on Edge Computing for Real-time Video Analytics

Edge computing has emerged as a promising solution for real-time video analytics, enabling efficient processing of high-definition video streams at the edge of the network. This thesis investigates the design and implementation of an edge computing system for real-time video analytics, addressing the challenges and limitations faced in traditional cloud-based approaches. The study aims to improve the efficiency and performance of video processing tasks by leveraging the capabilities of edge devices and sensors.

The literature review provides insights into edge computing, real-time video analytics, machine learning algorithms, and performance metrics relevant to the study. The system design and methodology chapter outlines the system architecture, data collection, preprocessing, real-time video stream processing, machine learning model selection, and system integration. The system implementation chapter details the hardware setup, software stack configuration, performance optimization, security measures, and integration with cloud services.

In conclusion, this thesis contributes to the field of edge computing for real-time video analytics by proposing an efficient system design and implementation approach. The findings offer valuable insights for researchers and practitioners in the field, and future research directions are suggested for further exploration and development.

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