###### Abstract  
As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge-server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments. p edestrian abnormal behavior analysis; edge-server architecture; edge computing; CNN; 3D CNN +
Footnote †: journal: Applied  
that their performance can degrade due to environmental factors such as changes in the shooting angle or lighting conditions. In addition, despite advancements in intelligent CCTV, existing analytics systems cannot adequately address the problem of limited server processing capacity as the number of connected CCTV cameras increases. To address these issues, this study proposes a real-time pedestrian abnormal behavior detection system based on an edge-server structure that ensures efficiency and scalability. The proposed system detects pedestrian abnormal behavior by linking edge devices and GPU servers. For the linkage of edge devices and GPU servers, the integration control server (ICS) is adopted; this is a two-stage analysis method that allows the GPU server to analyze the temporal section of the CCTV when pedestrian abnormal behavior occurs. The system is designed to balance the load on the server and minimize the network transmission data. This approach is intended to make the system scalable and easily adaptable to different environments and requirements. A similar approach is adopted in existing research [1; 2]. In [1], the authors describe a method that analyzes the edge device and then integrates the results into the cloud. However, these methods can be less accurate because they rely on analytics results from the edge and do not utilize high-performance analytics models. In [2], the researchers also layer multiple machine learning models to improve the prediction performance and reliability. However, this method may be limited by the computing environment because it requires the hierarchical division of the analytical models depending on the computing environment.