###### Abstract
To overcome these problems, the system proposed in this paper proposes an efficient two-stage analysis method that first detects abnormal behavior at the edge and then analyzes the detected data in detail on the server. At the edge device, an analysis model with low computing power but a high recall rate is utilized to analyze pedestrian abnormal behavior events as much as possible, and at the GPU server, an analysis model with high precision is utilized because it analyzes only the sections where pedestrian abnormal behavior is detected at the edge device. The proposed system first analyzes pedestrian abnormal behavior on the edge device. When pedestrian abnormal behavior is detected on the edge device, the information about the detected camera and detection section is sent to the GPU server through the ICS for further analysis. The edge device uses DNN-based object detection results to analyze pedestrian abnormal behavior in the rule base. The GPU server analyzes only the temporal section of the CCTV when pedestrian abnormal behavior is detected on the edge device, and it uses a deep learning-based mechanism to detect pedestrian abnormal behavior via DNN-based object detection and pose estimation model analysis. The initial analysis at the edge stage optimizes the amount of data sent to the server to reduce the network load and maximize the analysis efficiency of the entire system. Through experiments, we demonstrate that the system with the proposed edge-server architecture is more efficient and scalable than the use of the edge and server alone and that it can reduce the false positive rate of the pedestrian anomaly detection model. The proposed edge-server architecture-based pedestrian abnormal behavior detection system is innovative in that it reduces the amount of data transmission, shortens the reaction time of the system, and secures scalability by analyzing only the necessary segments on the server, unlike existing server analysis systems that allocate the resources of the analysis server even at unnecessary times by analyzing the connected CCTVs at all times. In addition, the contribution of such a system is that it can improve the accuracy of real-time data processing in large networks and reduce operating costs. These innovations and contributions of the proposed system are expected to secure the safety of citizens in future cities, reduce the workload of monitoring personnel in large-scale CCTV environments, and contribute to securing the safety of pedestrians. Section 2 reviews the current research on CCTV video analysis systems and pedestrian abnormal behavior detection. Section 3 outlines the application scenario and describes the proposed system. Section 4 evaluates the proposed system through a comparative analysis in terms of efficiency and scalability by comparing the experimental environment, dataset, evaluation metrics, performance for the edge and server, and performance after edge-server linkage. Section 5 discusses the challenges encountered, potential vulnerabilities, and areasfor improvement in the models. Finally, Section 6 concludes the paper by summarizing the research findings and highlighting the contributions of this work.