## 2 Related Works  
Modeling the coverage of surveillance cameras is essential for visual sensor applications and optimizing camera network placement. Various approaches have been proposed to address this challenge. A new approach called \"HybVOR\" is suggested for placing surveillance cameras using Voronoi-based 3D GIS techniques [1]. The goal is to achieve almost 100% coverage through three phases. One study proposed using imaging techniques to assist in the camera placement problem and to calculate camera coverage on a digitized 2D floor plan [2]. Another approach is a dynamic programming framework, which is introduced to optimize sensor coverage overlaps based on visual sensor parameters and the features of the monitored area [3]. This models the sensor coverage in a 2D space. In [4], an optimal policy is defined for solving the visual sensor coverage problem using a dynamic programming algorithm. It is compared to existing benchmarking placement optimization techniques to demonstrate its effectiveness in maximizing coverage for a set of predefined locations. The authors of [5] designed an approximation algorithm and an efficient heuristic algorithm based on pipage rounding to solve the maximum full-view target coverage problem in camera sensor networks, where each camera sensor has P working directions. In [6], the authors proposed integrating coverage inference and optimization methods into the existing GIS platform to promote various innovative applications in the camera planning area. An Alternate Global Greedy (AGG) algorithm was proposed in [7], a novel variant of the Global Greedy algorithm, to produce better coverage results. The experimental results show that the algorithms maximize coverage results with minimal overlap compared to existing greedy techniques. In [8], a camera network coverage model that considers obstacles and interesting areas of the environment is investigated and optimized by dynamic planning methods. The results show that this method can effectively improve the target capture rate of the network and observe the target from a better view. A study in [9] quantitatively evaluated the surveillance coverage of a CCTV system in an underground parking area and presented a full mathematical derivation for the resolution, which depends on the object's location and orientation as well as the camera's geometric model. Finally, the authors of [10] presented a distributed algorithm of perimeter surveillance that allows for the maintenance of total coverage in heterogeneous camera networks. This approach ensures maximum coverage with a minimum number of cameras. In the field of video surveillance, it is important to evaluate the performance of surveillance camera systems. To do this, various methods and measures have been proposed. These include evaluating the reliability and effectiveness of video content analysis algorithms by assessing their segmentation, tracking, and event detection capabilities [11]. Additionally, new methods have been introduced to compare the performance of object detection algorithms in video sequences fairly [12]. Furthermore, the physical performance of surveillance cameras can be evaluated through measurements of spatial resolution, sensitivity, energy resolution, and image quality [13]. Regularly evaluating and documenting the performance of video surveillance systems is crucial to measure advancements and improve the efficiency and accuracy of these systems [14]. Various fields have conducted extensive research on the evaluation of surveillance camera systems. In [15], a dual-camera real-time video surveillance system could achieve accurate statistical inference, automatic control parameter adjustment, and precise limit quantification by carefully selecting system modules and analyzing the impact of tuning parameters. Another work [16] proposes a quantitative approach to evaluate the efficacy of CCTV coverage, where the surveillance resolution is determined by object positions and orientations.