###### Abstract  
A surveillance camera is the typical device that makes up a surveillance camera system in a modern city. It will still be a representative surveillance unit in future scenarios such as in smart cities. Furthermore, as the demand for public safety increases, a massive number of surveillance cameras will be in use in the future, and an automated system that controls surveillance cameras intelligently will also be required. Meanwhile, installing a surveillance system without any verification system might not be cost-effective, so a simulation that evaluates the system's performance is required in advance. For this reason, we introduce how to simulate a surveillance area and evaluate surveillance performance in this paper to assess a surveillance system consisting of large amounts of surveillance cameras. Our simulator defined the surveillance area as a pair of two-dimensional planes, which depend on various camera-related configurations. Both surveillance areas are used to determine if the moving object belongs to the coverage of a surveillance camera. In addition, our simulator adopts several performance indices to evaluate a surveillance camera system in terms of target detection and quality. The simulation study provides comprehensive results on how various components of the surveillance system affect the performance of the surveillance system, leading to the conclusion that building a sophisticated scheme to control a large number of surveillance cameras can provide a cost-effective and reliable surveillance system for smart cities. massive CCTV; surveillance system; surveillance area; surveillance resolution +
Footnote †: journal:  
Therefore, surveillance cameras need to seamlessly track and detect objects such as moving pedestrians or vehicles. To realize the above scenario for public safety services, a precise calculation of the appropriate number and density of surveillance cameras, as well as the technical characteristics of the cameras, is required in advance, taking into account the features of the area and the object to be detected in the real world. Meanwhile, due to the development of deep learning and image processing technology, the technology to recognize specific objects or situations through surveillance cameras has already reached a high level. Still, existing research on real-time surveillance camera operation systems has only considered a few CCTV cameras or has focused on the efficient placement of cameras. The application environment is limited to indoor or building interior spaces. It is challenging to design a system that operates many surveillance cameras in real time because there is no established method to evaluate the performance of a wide range of surveillance cameras with different physical characteristics. For this reason, in this paper, as part of a digital twin world that mimics the real world, as shown in Figure 1, we implement a system-level simulator by modeling the surveillance performance of surveillance cameras, detection targets, and the local environment and devise several performance metrics to measure the detection performance and quality of the system to evaluate the performance of the system according to the physical characteristics of surveillance cameras. That is, we aim to evaluate a surveillance system which consists of a vast number of surveillance cameras. The surveillance area is determined by two-dimensional planes (horizontal and vertical), indicating whether the target object falls within the surveillance camera's coverage. The surveillance resolution is represented by the precision level shown on the display, which shows the target's length per pixel in both the horizontal and vertical aspects. To determine the two surveillance indices, we consider several factors, such as the installed location, viewing angle, sensor format, focal length, and more. Our simulation approach aims to assess the surveillance system's performance in various scenarios to determine its effectiveness in detecting and tracking criminal activities.