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Introduction:

Video summarization and highlight extraction are important tasks in the field of video analysis. With the increasing amount of video content available on the internet, there is a growing need for automated systems that can efficiently summarize and extract key highlights from videos. Deep learning algorithms have been shown to be effective in various computer vision tasks, including video analysis. In this thesis, we aim to develop a deep learning-based system for video summarization and highlight extraction.

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 video summarization and highlight extraction
2.2 Traditional methods for video summarization
2.3 Deep learning algorithms for video analysis
2.4 Video summarization using deep learning
2.5 Highlight extraction using deep learning
2.6 Evaluation metrics for video summarization
2.7 Challenges in video summarization and highlight extraction
2.8 State-of-the-art approaches in video summarization
2.9 Comparison of different methods
2.10 Gaps in existing research

Chapter 3: Research Methodology
3.1 Introduction
3.2 Data collection and preprocessing
3.3 Deep learning models for video summarization
3.4 Training and testing procedures
3.5 Evaluation metrics
3.6 Parameter tuning
3.7 Experimental setup
3.8 Ethical considerations
3.9 Data analysis techniques

Chapter 4: Discussion of Findings
4.1 Introduction
4.2 Performance evaluation of the proposed system
4.3 Comparison with existing methods
4.4 Analysis of results
4.5 Interpretation of findings
4.6 Limitations of the study
4.7 Future research directions
4.8 Practical implications
4.9 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contribution to the field
5.3 Implications for practice
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview:

The rapid growth of video content on the internet has made video summarization and highlight extraction crucial tasks for efficient video analysis. In this thesis, we aim to develop a deep learning-based system for video summarization and highlight extraction. We will begin by providing an introduction to the topic, discussing the background of the study, stating the problem statement, highlighting the objectives, limitations, scope, and significance of the study, and outlining the structure of the thesis.

In the literature review chapter, we will examine traditional methods for video summarization, explore the use of deep learning algorithms in video analysis, review existing research on video summarization and highlight extraction using deep learning, and discuss evaluation metrics, challenges, state-of-the-art approaches, and gaps in the literature.

The research methodology chapter will detail our data collection and preprocessing procedures, deep learning models for video summarization, training and testing processes, evaluation metrics, parameter tuning, experimental setup, ethical considerations, and data analysis techniques.

In the discussion of findings chapter, we will present and analyze the performance of our proposed system, compare it with existing methods, interpret the results, discuss the limitations of the study, suggest future research directions, and draw practical implications from our work.

Finally, in the conclusion and summary chapter, we will summarize the key findings of our study, highlight our contribution to the field, discuss the implications for practice, provide recommendations for future research, and conclude the thesis.

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