Multi-Objective Optimization of CCTV Placement: A Case Study at Bukidnon State University

Abstract

Optimizing closed-circuit television (CCTV) camera placement in open-field environments is essential to enhance surveillance coverage while minimizing costs. This study addressed this challenge using a multi-objective optimization approach, employing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to balance coverage efficiency and cost-effectiveness. The method integrated grid-based layouts with varying densities and allowable camera orientations to identify optimal camera configurations. Bullet and omnidirectional cameras, characterized by differing angles of view (AOV) and depth of field (DOF), were strategically deployed to achieve comprehensive surveillance coverage. To validate this approach, the study focused on Bukidnon State University (BukSU), where wide-open campus areas presented unique monitoring challenges. Using Google Satellite imagery, optimal camera placements were determined, and the impact of grid density and camera orientation flexibility on cost and coverage was evaluated. Results showed that dense grid patterns with finer camera orientations (e.g., 45° intervals) maintained stable costs while improving positioning flexibility and reducing the need for additional cameras. In contrast, less dense grids with limited camera orientations (e.g., 90° intervals) increased costs due to the higher number of cameras required to achieve adequate coverage. Based on simulations, this study provided valuable insights for institutions aiming to enhance security in large, open spaces. By emphasizing the importance of grid density and flexible camera orientations, it contributed to the broader field of cost-efficient, large-scale surveillance optimization and offers a practical framework for addressing similar challenges in other settings.