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Table of Contents

Chapter 1: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Objectives of the Study
1.4 Significance of the Study
1.5 Limitations of the Study
1.6 Scope of the Study

Chapter 2: Literature Review
2.1 Overview of Computer Vision
2.2 Applications of Computer Vision in Surveillance
2.3 Techniques and Algorithms in Computer Vision for Surveillance
2.4 Previous Studies on Autonomous Surveillance using Computer Vision

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Experimental Setup

Chapter 4: Discussion of Findings
4.1 Analysis of Data
4.2 Interpretation of Results
4.3 Comparison with Previous Studies
4.4 Implications of Findings

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Recommendations for Future Research
5.4 Practical Implications

Brief Overview on Computer Vision for Autonomous Surveillance

Computer Vision for Autonomous Surveillance is an emerging technology that combines computer vision techniques with artificial intelligence to monitor and analyze video feeds in real-time. This technology has significant applications in various sectors such as security, retail, transportation, and healthcare.

The main objective of using computer vision for autonomous surveillance is to detect and track objects, identify patterns, and make decisions based on the analysis of the visual data. This can help in improving the efficiency of surveillance systems by reducing human intervention and increasing the accuracy of threat detection.

Some of the key techniques and algorithms used in computer vision for autonomous surveillance include object detection, tracking, classification, and recognition. These techniques enable the system to detect and identify objects of interest, such as intruders, suspicious activities, or unusual behavior.

Previous studies have shown promising results in the use of computer vision for autonomous surveillance, with increased accuracy in threat detection and reduced false alarms. However, there are still challenges and limitations that need to be addressed, such as occlusions, lighting conditions, and privacy concerns.

In this study, we will conduct a comprehensive review of the literature on computer vision for autonomous surveillance, analyze the current techniques and algorithms, and propose a research methodology to investigate the effectiveness of this technology in real-world scenarios. The findings of this study will provide valuable insights for researchers, practitioners, and decision-makers in the field of autonomous surveillance.

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