Abstract:
Object recognition and tracking are fundamental tasks in computer vision with numerous applications, such as surveillance, robotics, and autonomous vehicles. This project aims to develop a computer vision system that can accurately recognize and track objects in real-time. By leveraging deep learning algorithms and image processing techniques, the proposed system will analyze video streams, detect objects of interest, and track their movements over time. The system’s accuracy and efficiency will be evaluated using benchmark datasets, and its potential for integration into real-world applications will be explored.

Table of Contents:
Chapter 1: Introduction
1.1 Background and Motivation
1.2 Problem Statement
1.3 Objectives
1.4 Scope and Limitations

Chapter 2: Literature Review
2.1 Overview of Object Recognition and Tracking
2.2 Image Processing Techniques for Object Detection
2.3 Deep Learning Algorithms for Object Recognition
2.4 Tracking Algorithms and Techniques

Chapter 3: Methodology
3.1 Data Collection and Preprocessing
3.2 Object Recognition Model Development
3.3 Object Tracking Model Development

Chapter 4: System Implementation and Evaluation
4.1 System Architecture and Integration
4.2 Real-Time Object Recognition and Tracking
4.3 Performance Evaluation and Comparison
4.4 Robustness and Adaptability Analysis

Chapter 5: Conclusion and Future Work
5.1 Summary of Findings
5.2 Contributions and Implications
5.3 Limitations and Challenges
5.4 Future Directions for Research