## 3 Proposed System
### Edge-Server Architecture-Based Pedestrian Abnormal Behavior Detection System  
This section describes the structure of the proposed system in detail. Figure 1 shows the structure of the proposed system. The system is divided into the CCTV, edge, integration control server (ICS), and GPU server. The CCTV transmits video streams to the edge using a real-time streaming protocol. Figure 2 provides a detailed description of the analytics engine that constitutes the analytics module of the edge. The structure shown in the figure illustrates the analytics engine configured for the experiments described in this paper. The number of analytics engines may vary depending on the pedestrian anomaly events to be analyzed, and object tracking or other preprocessing engines may be added depending on the preprocessing modules required by the pedestrian anomaly engine. In this paper, to experiment with the structure of the proposed system, the edge analysis module is configured to perform pedestrian abnormal behavior analysis through object detection, object tracking, and pedestrian abnormal behavior detection engines. In addition, more than one CCTV camera can be connected to the edge, and the number of CCTV connections is determined by the frame throughput that the object detection engine and pedestrian abnormal behavior detection engine of the edge can handle. In Section 4, we discuss this in more detail. The edge's pedestrian anomaly detection engine is rule-based, allowing real-time analysis in environments with low computing power. The edge receives CCTV video streams in real time and delivers the original video to the ICS; extracts frames from the received video stream; performs object detection, object tracking, and pedestrian abnormal behavior detection analysis based on these frames; and delivers the analysis results to the ICS's abnormal behavior event receiver (ABER). Figure 1: Overview of edge–server architecture-based pedestrian abnormal behavior detection system. The ICS is composed of four main components: the abnormal behavior event receiver (ABER), the server analysis task scheduler (SATS), the video storage manager (VSM), and the result notification dispatcher (RND). The ABER collects analytics results from the edge's pedestrian anomaly detection engine. When a pedestrian anomaly event occurs in the edge's analytics engine, the ABER sends an analysis request to the SATS, which sends the corresponding CCTV and event time to the GPU server's analytics module for analysis. If no idle GPU exists, the analytics request is assigned to the server analytics module with the most analytics requests. The VSM stores and manages the video streams from the edge connected to the ICS. The pedestrian anomaly analysis results analyzed by the edge device send an analysis start event to the ABER at the beginning of the anomaly detection process and an analysis end event to the ABER at the end of the anomaly detection process. When the ABER receives a pedestrian anomaly event from the edge, it forwards an analysis request to the SATS. The algorithm enabling the SATS to assign analysis requests to the GPU server's analysis module is described in Algorithm 1. The SATS sends the CCTV section to be analyzed from the video database connected to the VSM and the GPU server's analysis module through the HLS protocol. If the type of analysis request received from the ABER starts, it is assigned to the analysis module currently connected to the SATS, which has no analysis requests in process or the smallest number of analysis requests. If all analytics modules are processing the same number of analytics requests, the analytics request is assigned to the analytics module with the fastest analysis speed, considering the interval and start time of the analytics request. The analysis module on the GPU server selected by the SATS receives the video address from the video database and analyzes the pedestrian anomaly.