The quest for optimal camera placement for effective surveillance has long been an academic pursuit. However, the advent and proliferation of visual sensor networks have propelled this issue into the practical realm, necessitating solutions that are both viable and implementable. The complexity of formulating camera placement as a binary integer programming (BIP) problem, despite its adaptability, often leads to NP-hard scenarios, which present significant challenges for achieving exact solutions, particularly for large-scale applications. This study introduces a binary integer programming technique to strategically minimize the number of CCTV cameras while ensuring adequate surveillance coverage, with a particular focus on the Clemson University campus. Using integer linear programming (ILP), our approach overcomes the traditional hurdles associated with BIP. By constructing a systematic model that leverages the predictability of ILP, the paper demonstrates a method that is both computationally manageable and accurate in deducing the minimum number of cameras needed for full coverage. In operationalizing this model, the study made certain assumptions about the Clemson University campus street layout. It was presupposed that the average length of each street segment was known, and the fields of view for camera installation were predefined. A key presumption was that cameras should be stationed at every road intersection, capturing two street perspectives. This strategy ensures that each street segment is monitored by at least one camera. From this analysis, it was inferred that an optimal, minimal number of cameras could be strategically placed at various key points to provide comprehensive surveillance coverage. This inference is crucial, as it aligns with the objective of deploying a surveillance system that is not only effective but also economical and efficient in its use of resources. This methodology significantly advances the practical deployment of surveillance systems, particularly in complex and expansive settings, where the intricate interplay of coverage areas, camera fields of view, and the necessity for minimization of resources is paramount. The proposed model promises a sophisticated yet practical solution to the pervasive challenge of security surveillance, marrying theoretical rigor with practical application