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Introduction

Computer vision is a rapidly growing field with applications in various industries such as robotics, surveillance, automotive, healthcare, and more. Object tracking is a fundamental task in computer vision that involves the process of locating a target object in a sequence of frames. The ability to accurately track objects in real-time has numerous practical implications, such as in security surveillance, autonomous driving, and interactive gaming.

This thesis focuses on the development of a computer vision system for object tracking. The system utilizes state-of-the-art algorithms and techniques to track objects in complex and dynamic environments. The goal of this research is to improve the accuracy and efficiency of object tracking algorithms, ultimately leading to enhanced performance in real-world applications.

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 Introduction to object tracking
2.2 History of object tracking
2.3 Traditional object tracking methods
2.4 Machine learning-based object tracking
2.5 Deep learning for object tracking
2.6 Challenges in object tracking
2.7 Evaluation metrics for object tracking
2.8 Applications of object tracking
2.9 Recent advancements in object tracking
2.10 Summary of literature review

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Data collection and preprocessing
3.3 Feature extraction
3.4 Object detection
3.5 Object representation
3.6 Motion estimation
3.7 Object tracking algorithm
3.8 Performance evaluation
3.9 Experimental setup
3.10 Summary of methodology

Chapter 4: System Implementation
4.1 Software and hardware requirements
4.2 Implementation of data collection
4.3 Implementation of feature extraction
4.4 Implementation of object detection
4.5 Implementation of object representation
4.6 Implementation of motion estimation
4.7 Implementation of object tracking algorithm
4.8 Testing and validation
4.9 Results and analysis
4.10 Summary of system implementation

Chapter 5: Conclusion and Summary
5.1 Summary of research findings
5.2 Contributions of the study
5.3 Future directions
5.4 Conclusion

Thesis Overview

Computer vision is a rapidly evolving field that has found applications in various domains, including object tracking. Object tracking is a fundamental task in computer vision, with applications in surveillance, autonomous driving, augmented reality, and more. This thesis focuses on the development of a computer vision system for object tracking, with the aim of improving the accuracy and efficiency of tracking algorithms.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review on object tracking, covering traditional methods, machine learning-based approaches, deep learning techniques, challenges, evaluation metrics, applications, recent advancements, and a summary of the literature.

Chapter 3 discusses the system design and methodology, including the system architecture, data collection, preprocessing, feature extraction, object detection, representation, motion estimation, tracking algorithm, performance evaluation, experimental setup, and methodology summary. Chapter 4 focuses on the system implementation, detailing the software and hardware requirements, data collection, feature extraction, object detection, representation, motion estimation, tracking algorithm, testing, validation, results, analysis, and implementation summary.

Chapter 5 concludes the thesis with a summary of research findings, contributions, future directions, and a final conclusion. The overall goal of this thesis is to contribute to the advancement of object tracking algorithms in computer vision, leading to improved performance and applicability in real-world scenarios.

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