## 4 Experiments
### System Efficiency Analysis and Performance Comparison
Table 4 shows the performance of the edge and server alone and after edge-server linkage. The pedestrian abnormal behavior analysis model of the edge is a rule-based model. However, it is designed with higher recall than precision, and the server's pedestrian abnormal behavior analysis model is designed with lower recall but higher precision. Pedestrian anomaly analysis using only general edges has a high recall, so frequent alarms are generated; when the controllers in the control center receive frequent alarms, fatigue, and distraction can slow down the response to actual important events. This, in turn, reduces the overall effectiveness of the system and risks reducing the ability to respond appropriately to emergencies. If the server is used alone, the frequency of alarms is reduced because the pedestrian anomaly analysis model is designed to prioritize precision. However, the server must process all connected CCTV cameras, which creates a significant load. Consequently, a server expansion cost may be incurred, as the server needs to be upgraded to connect additional CCTV cameras. Table 4 shows the processing time versus the video length used in the experiment. In the table, in the term video length \(\times\) CCTV, video length is the average length of each video, and CCTV is the number of video streams. The experimental results show that processing 20 videos with the edge alone takes 294 s, with an average of 313 s, but the F1 score is the lowest. The server alone takes over three times as long to process 20 videos but performs better than the edge. On the other hand, we can see that using both the edge and server results in near real-time processing but with improved performance over the edge and server alone. Based on these experimental results, the edge-server system is designed to combine the advantages of high recall from the edge and high precision from the server, thus minimizing the occurrence of unnecessary alarms while not missing important events. The results for the edge-server system show that the recall performance is improved compared to that of the server alone due to the reduction in false alarms in the combined system. Therefore, it can be seen that the system proposed in this paper can help controllers focus on critical events and respond quickly, improving the overall system's performance and reliability. Table 5 compares the performance of the edge and server alone and that after edge-server coupling for day and night videos. The experimental results clearly show that the performance varies between day and night; they provide an essential baseline for the evaluation of the adaptability and effectiveness of each system under different environmental conditions. When the edge is used independently, it tends to have high recall, which enables it to detect pedestrian anomalies without missing them. When the server is used independently, it tends to have high precision, which enables it to detect pedestrian anomalies with high accuracy. On the other hand, the integrated edge-server architecture shows a large performance improvement in terms of recall and precision compared to the standalone system. This proves that the proposed system is able to minimize unnecessary daytime and nighttime alarms while not missing important events. These results demonstrate the applicability and practicality of the integrated system in real-world urban environments while contributing to improved pedestrian safety and public security.