Implementation of AI-Based Smart Surveillance Cameras for Enhancing University Security: A Case Study of Federal University, Lafia (Lafia LGA, Nasarawa State)
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Background of the Study
University campuses are often large and complex environments that require effective security measures to ensure the safety of students, faculty, and staff. Traditional security measures, such as manual patrols and closed-circuit television (CCTV) cameras, are limited in their ability to monitor vast areas efficiently (Hassan & Liu, 2023). AI-based smart surveillance cameras offer an advanced solution, utilizing computer vision and deep learning algorithms to detect unusual behavior, identify individuals, and provide real-time alerts. These systems can automatically analyze video footage, reducing the need for constant human monitoring and allowing security personnel to respond more effectively to potential threats (Bello et al., 2024). By integrating AI into surveillance systems, universities can enhance their ability to prevent and respond to incidents, thereby improving campus safety.
Federal University, Lafia, located in Lafia LGA, Nasarawa State, has been facing challenges with campus security due to the growing student population and increasing incidents of theft and violence. The university has recognized the need for a more advanced and efficient security system, one that can provide real-time monitoring of the campus environment and proactively address security concerns. This study aims to explore the implementation of AI-based smart surveillance cameras to enhance campus security.
Statement of the Problem
Federal University, Lafia faces significant challenges in managing campus security, including incidents of theft, violence, and unauthorized access to restricted areas. Traditional security measures have proven insufficient in providing comprehensive surveillance across the university’s expansive campus. AI-based smart surveillance cameras offer a solution to address these security gaps, but their implementation and effectiveness in the university setting have not been fully explored.
Objectives of the Study
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To implement an AI-based smart surveillance system at Federal University, Lafia to enhance campus security.
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To assess the effectiveness of the AI-based surveillance cameras in detecting and preventing security incidents.
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To evaluate the impact of AI-based surveillance on the university's overall security management and incident response.
Research Questions
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How effective are AI-based smart surveillance cameras in enhancing campus security at Federal University, Lafia?
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What impact does the AI-based surveillance system have on the prevention and detection of security incidents?
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How can AI-based surveillance cameras improve the university's response time to security threats?
Significance of the Study
This study will provide insights into the use of AI in campus security, demonstrating how smart surveillance systems can enhance the safety of university environments. The findings could help Federal University, Lafia and other universities optimize their security infrastructure, reducing incidents and ensuring a safer campus for all stakeholders.
Scope and Limitations of the Study
The study will focus on implementing and evaluating AI-based smart surveillance cameras for campus security at Federal University, Lafia, located in Lafia LGA, Nasarawa State. The research will assess the effectiveness of the system in detecting and preventing security incidents but will not address broader security management policies or non-AI-related security measures.
Definitions of Terms
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AI-Based Smart Surveillance: Surveillance systems powered by artificial intelligence that use computer vision and machine learning to detect and analyze security threats in real-time.
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Campus Security: Measures and practices implemented to ensure the safety of students, staff, and visitors on a university campus.
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Computer Vision: A field of AI that enables computers to interpret and analyze visual information, such as video footage, to detect objects, behaviors, and patterns.
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