## 3 Proposed System
### Application of the Proposed System  
In this section, we describe the application scenario considered for the proposed system. We investigated the Distinction Control Center in Seoul, Korea, to build the application scenario. The existing control system used by the center to monitor the city consists of four people working in three shifts of eight hours each, and each person monitors a program that contains 16 small screens that show different CCTV sections every five seconds. Moreover, when a police report or request is made, one of the controllers monitors the CCTV in the area where the incident occurred. In order to apply pedestrian anomaly analysis to such a system, it is necessary to utilize existing CCTV, so the installation of additional intelligent CCTV devices at an additional cost is not preferable for control agencies. Therefore, it is necessary to connect the edge devices to the existing CCTV or to connect an analysis server for analysis. Therefore, in this paper, we consider a scenario in which the edge devices first analyze the video streams of the CCTV of such an existing system. Only the time sections of the CCTV where the pedestrian abnormal behavior events occurred are analyzed by the analysis module of the GPU server. Finally, the existing control system is notified when pedestrian abnormal behavior occurs. In previous studies, methods that utilized only edge devices showed fast processing speeds but low accuracy, and frequent alarms could lead to increased fatigue and distraction, which could slow down the response to actual important events. In addition, the existing server-based system provides high accuracy. However, it requires server expansion as the number of CCTV devices increases; moreover, as the number of CCTV devices increases, the number of analyzable servers must be increased, which leads to increased costs. The system proposed in this paper has a cost-saving effect, scalability, and efficiency because it analyzes only the temporal segment of the CCTV with a GPU server only when the edge device detects pedestrian abnormal behavior. In addition, the analysis module used by the edge device can process multiple CCTV images on one edge, depending on the frame throughput required by the analysis engine, and the server analysis module performs analysis only when a pedestrian abnormal behavior event occurs as a result of the edge analysis. This fast preprocessing at the edge and the transmission of only the necessary data to the server maximize the analysis efficiency, reduce the load on the server, and reduce the cost. Therefore, the proposed system can enhance pedestrian safety and detect abnormal real-time behavior in various urban environments. In urban areas, thousands of individuals walk on the streets each day, which brings a variety of hazards. Through real-time video analysis, the system can reduce the risk of accidents at intersections and pedestrian walkways, detect dangerous situations in front of crosswalks in advance, and send appropriate warning signals to draw the attention of both drivers and pedestrians. In educational institutions such as schools and university campuses, protecting the safety of students is a major priority. This system can help to identify conflicts between students, urgent health issues, or other emergencies on campus in real time so that authorities can respond immediately. For example, if a potential fight or medical emergency is detected on campus, the system can automatically alert the relevant authorities for a quick response. Shopping malls and commercial facilities also often see large volumes of customers, which can lead to congestion and incidents such as theft. This system analyzes customer movement routes and congestion levels and notifies managers so that they can efficiently manage congestion and prevent theft. It also ensures safety in commercial facilities by responding quickly in an emergency and providing customers with a safe escape route. This system can function as a critical element of public safety in smart cities.