## 3 Proposed System
### Edge-Server Architecture-Based Pedestrian Abnormal Behavior Detection System
If only existing edge devices are utilized, the number of CCTVs that can be accommodated in one edge device by the Formula (1) can be used to check the capacity of the system. However, since the system proposed in this study is a structure in which edge devices and GPU servers are linked, the number of events and event duration must be considered to evaluate the scalability of the system. \[N_{\text{edge}_{\text{cvt}}}=\frac{f_{\text{edge}_{\text{need}}}}{f_{\text{edge }_{\text{cap}}}} \tag{1}\]  
It analyzes it on the GPU-equipped server when a pedestrian anomaly event is detected on the edge device, so the system's scalability can be evaluated according to the number of pedestrian anomalies and the duration of the anomaly in the area in which the system is installed. Formula (2) denotes the average number of events (\(\lambda\)) during the experimental time in the region in which the system is installed, the time to run the experiment (\(T\)), and the duration of each event (\(d\)). The number of analytics modules on the GPU server (\(N_{server}\)) is based on the number of frames per second that can be processed by the analytics modules on the GPU server (\(f_{\text{server}_{\text{cap}}}\)) and the number of frames per second needed for analysis (\(f_{\text{server}_{\text{med}}}\)). For a centralized server-based analysis system, the number of servers should be specified according to the number of frames that can be processed and the number of frames required for analysis. However, for the system proposed in this study, the number of analysis modules on the GPU server will vary depending on the number of events and the duration of each event, as the system only analyzes the time bins of the CCTV devices that detect pedestrian anomalies on the edge devices. \[N_{\text{server}}=\left\lceil\frac{f_{\text{server}_{\text{med}}}\times\frac{ \lambda}{T}\times d}{f_{\text{server}_{\text{cap}}}}\right\rceil \tag{2}\]  
In particular, the system proposed in this research greatly improves the efficiency of real-time data processing by reducing unnecessary data transmission through initial analysis at the edge and processing only more complex data on the server, thereby improving the responsiveness and data processing capabilities of the entire system. Compared to the existing centralized server system that utilizes a deep learning model with high complexity, the proposed system is innovative in terms of data processing capability and scalability of the entire system in that the existing system requires the server to continuously analyze the video stream when the video stream to be analyzed is recorded and processed in real-time. In contrast, the proposed system analyzes only the sections that need to be analyzed in the server, which requires the server to always analyze all CCTVs connected to the system. In the proposed system, the edge stage detects pedestrian abnormalities based on rules based  
Figure 3: Analysis module structure of the GPU server. This figure shows how the results of the pedestrian abnormal behavior analysis performed on the edge device are sent to the GPU server for processing. The server performs analysis based on the requirements of the pedestrian abnormal behavior analysis engine, which requires additional preprocessing engines such as pose estimation. Based on the analysis results sent from the edge, the potent processing power inside the GPU server is used to perform more sophisticated analyses, including pose estimation, to analyze the pedestrian abnormal behavior further. The events detected by the server are sent back to the ICS, which ultimately notifies the controllers of the pedestrian’s abnormal behavior. on object recognition and tracking, and when pedestrian abnormalities are detected, the CCTVs with detected pedestrian abnormalities and the detection time are sent to the server for in-depth analysis. This distributed processing method minimizes the network load and maximizes the entire system's performance by using optimized algorithms at each stage.