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Introduction:

In recent years, advances in deep learning technology have enabled significant progress in the field of computer vision, particularly in the development of object detection and tracking systems. Autonomous vehicles, also known as self-driving cars, are one of the most promising applications of this technology. Object detection and tracking are crucial components of autonomous vehicles, as they enable the vehicle to perceive and understand its environment in real-time, making informed decisions to navigate safely and efficiently.

This thesis aims to develop a deep learning-based system for object detection and tracking in autonomous vehicles. The system will utilize state-of-the-art deep learning algorithms and techniques to accurately detect and track various objects, such as pedestrians, vehicles, and obstacles, on the road. By improving the reliability and accuracy of object detection and tracking, the system will enhance the overall performance and safety of autonomous vehicles.

Table of Contents:

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 detection and tracking in autonomous vehicles
2.2 Deep learning in computer vision
2.3 State-of-the-art deep learning models for object detection and tracking
2.4 Challenges in object detection and tracking in autonomous vehicles
2.5 Existing approaches and systems for object detection and tracking
2.6 Performance evaluation metrics for object detection and tracking
2.7 Recent research trends in deep learning-based object detection and tracking
2.8 Applications of deep learning in autonomous vehicles
2.9 Integration of object detection and tracking with other autonomous vehicle subsystems
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection and preprocessing
3.3 Object detection model selection
3.4 Training and validation process
3.5 Object tracking algorithm selection
3.6 Performance evaluation methodology
3.7 Hardware and software requirements
3.8 Ethical considerations
3.9 Limitations of the research methodology

Chapter 4: Discussion of Findings
4.1 Object detection performance analysis
4.2 Object tracking performance analysis
4.3 Comparison with existing approaches
4.4 Evaluation of system efficiency and scalability
4.5 System integration challenges
4.6 Future research directions
4.7 Implications for autonomous vehicles industry
4.8 Recommendations for further development

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Conclusion
5.3 Contributions to the field
5.4 Limitations of the study
5.5 Future research directions
5.6 Final remarks

Thesis Overview:

The development of a deep learning-based system for object detection and tracking in autonomous vehicles is a critical aspect of enhancing the safety and efficiency of autonomous driving technology. This thesis will focus on leveraging the power of deep learning algorithms to detect and track objects in real-time, enabling autonomous vehicles to make informed decisions and navigate complex environments.

In Chapter 1, the introduction will provide an overview of the research problem, objectives, scope, and significance of the study. The literature review in Chapter 2 will explore existing approaches, techniques, and challenges in object detection and tracking for autonomous vehicles. Chapter 3 will detail the research methodology, including data collection, model selection, training process, and performance evaluation. Chapter 4 will present a comprehensive discussion of the findings, including object detection and tracking performance analysis, system efficiency, integration challenges, and future research directions. Finally, Chapter 5 will conclude the thesis with a summary of key findings, contributions, limitations, and recommendations for further development in the field.

Overall, this thesis aims to contribute to the advancement of autonomous driving technology by developing a robust and efficient deep learning-based system for object detection and tracking in autonomous vehicles. By addressing the key challenges and limitations in current systems, the research outcomes will provide valuable insights and recommendations for the industry and academia.

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