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Introduction

Object tracking in video streams is an essential task in computer vision and image processing, with applications in surveillance, autonomous driving, augmented reality, and sports analysis, among others. The ability to accurately track objects in real-time video streams is crucial for various tasks such as target recognition, behavior analysis, and anomaly detection. However, the challenges associated with object tracking, such as occlusions, scale variations, illumination changes, and motion blur, make it a complex and challenging problem to solve.

This thesis aims to investigate and analyze various object tracking algorithms, methods, and techniques employed in video streams. The study will focus on evaluating the performance of different tracking methods in terms of accuracy, efficiency, and robustness under different scenarios and conditions. Additionally, the research aims to propose novel approaches and enhancements to existing object tracking algorithms to improve their performance and reliability.

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 object tracking
2.2 Types of object tracking algorithms
2.3 Traditional object tracking methods
2.4 Deep learning-based object tracking
2.5 Evaluation metrics for object tracking
2.6 Challenges in object tracking
2.7 Applications of object tracking
2.8 Comparative analysis of object tracking algorithms
2.9 Recent advancements in object tracking
2.10 Gaps in existing literature

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection and preprocessing
3.3 Experimental setup
3.4 Performance evaluation metrics
3.5 Implementation details
3.6 Data analysis techniques
3.7 Ethical considerations
3.8 Limitations of the research methodology

Chapter 4: Discussion of Findings
4.1 Performance comparison of object tracking algorithms
4.2 Analysis of experimental results
4.3 Impact of dataset characteristics on tracking performance
4.4 Proposed enhancements to existing object tracking algorithms
4.5 Challenges and limitations encountered
4.6 Future research directions
4.7 Practical implications of the findings

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the field of object tracking
5.3 Implications for practice and future research
5.4 Conclusion

Thesis Overview:

Object tracking in video streams is a crucial topic in computer vision research, with implications in various real-world applications. This thesis aims to provide a comprehensive investigation into object tracking algorithms, methods, and techniques, with a focus on evaluating their performance and proposing enhancements to improve tracking accuracy, efficiency, and robustness.

The literature review will cover various aspects of object tracking, including traditional tracking methods, deep learning-based approaches, evaluation metrics, challenges in tracking, and recent advancements in the field. By analyzing existing literature, this study will identify gaps and limitations in current research, leading to the development of novel research methodologies and approaches.

The research methodology chapter will outline the experimental design, data collection, preprocessing, and evaluation metrics used to assess the performance of different object tracking algorithms. The chapter will also discuss ethical considerations, limitations of the methodology, and data analysis techniques employed in the study.

The discussion of findings chapter will provide a detailed analysis of experimental results, performance comparison of tracking algorithms, and proposed enhancements to existing methods. By critically examining the findings, this study aims to contribute to the advancement of object tracking research and provide insights for future research directions.

In conclusion, this thesis will summarize key findings, contributions to the field, implications for practice, and future research directions. By addressing the challenges and limitations in current object tracking research, this study aims to provide valuable insights and recommendations for improving object tracking in video streams.

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