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Introduction

In recent years, deep learning has become the go-to method for various computer vision tasks, including human pose estimation and tracking. Human pose estimation and tracking involve the process of detecting and tracking human body joints in a video sequence. This has numerous applications in fields such as surveillance, sports biomechanics, human-computer interaction, and healthcare. Traditional methods for pose estimation and tracking often rely on handcrafted features and require complex algorithms, making them less accurate and efficient.

The aim of this thesis is to develop a deep learning-based system for video-based human pose estimation and tracking. Deep learning has shown promising results in various computer vision tasks due to its ability to automatically learn features from data and generalize well to new, unseen examples. By utilizing deep learning techniques, we aim to improve the accuracy and robustness of human pose estimation and tracking in videos.

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 human pose estimation and tracking
2.2 Traditional methods for pose estimation and tracking
2.3 Deep learning approaches for pose estimation and tracking
2.4 State-of-the-art deep learning models for pose estimation and tracking
2.5 Evaluation metrics for pose estimation and tracking
2.6 Challenges and limitations in existing methods
2.7 Recent advancements in human pose estimation and tracking
2.8 Applications of pose estimation and tracking
2.9 Summary of literature review
2.10 Research gaps and opportunities

Chapter 3: Research Methodology
3.1 Data collection and preprocessing
3.2 Model architecture design
3.3 Training procedure
3.4 Data augmentation techniques
3.5 Evaluation metrics selection
3.6 Experimental setup
3.7 Performance evaluation criteria
3.8 Comparison with baseline methods

Chapter 4: Discussion of Findings
4.1 Analysis of experimental results
4.2 Comparison with state-of-the-art methods
4.3 Performance evaluation and benchmarking
4.4 Error analysis and model interpretation
4.5 Discussion on the impact of key parameters
4.6 Limitations and challenges encountered
4.7 Future research directions
4.8 Practical implications and applications

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions of the study
5.3 Implications for future research
5.4 Practical implications for the industry
5.5 Conclusion and recommendations

Thesis Overview

Developing a deep learning-based system for video-based human pose estimation and tracking is a vital research topic in the field of computer vision. Human pose estimation and tracking have a wide range of applications in various domains such as surveillance, sports analysis, healthcare, and virtual reality. Traditional methods for pose estimation and tracking have limitations in terms of accuracy and efficiency, making them less suitable for real-world applications.

In this thesis, we propose to develop a deep learning-based system for video-based human pose estimation and tracking. Deep learning has shown exceptional performance in various computer vision tasks, and we aim to leverage its capabilities to improve the accuracy and robustness of pose estimation and tracking in videos. The thesis will consist of five chapters, including an introduction, literature review, research methodology, discussion of findings, and conclusion.

The introduction chapter provides an overview of the research topic, background of the study, problem statement, objective of the study, limitations, scope, significance, and structure of the thesis. The literature review chapter will discuss the existing methods for pose estimation and tracking, deep learning approaches, evaluation metrics, challenges, recent advancements, and research gaps.

The research methodology chapter will detail the data collection and preprocessing, model architecture design, training procedure, evaluation metrics selection, experimental setup, performance evaluation criteria, and comparison with baseline methods. The discussion of findings chapter will analyze the experimental results, compare with state-of-the-art methods, evaluate performance, analyze errors, discuss key parameters, address limitations, suggest future research directions, and practical implications.

The conclusion and summary chapter will provide a summary of key findings, contributions of the study, implications for future research, practical implications for the industry, conclusion, and recommendations. By developing a deep learning-based system for video-based human pose estimation and tracking, we aim to contribute to the advancement of computer vision research and applications in real-world scenarios.

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