## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Abnormal Behavior**} & \multirow{2}{*}{**Day or Night**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**FN**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1 Score**} \\  & & Standalone Edge & 192 & 41 & 27 & 0.875 & 0.824 & 0.849 \\  & Day & Standalone Server & 199 & 39 & 10 & 0.954 & 0.836 & 0.891 \\  & & Integrated edge–server (Ours) & 190 & 27 & 14 & 0.931 & 0.8756 & 0.902 \\ \cline{2-8}  & \multirow{4}{*}{Night} & Standalone Edge & 96 & 29 & 5 & 0.950 & 0.764 & 0.847 \\  & & Standalone Server & 98 & 12 & 13 & 0.881 & 0.888 & 0.884 \\  & & Integrated edge–server (Ours) & 96 & 13 & 7 & 0.933 & 0.880 & 0.906 \\ \hline \multirow{4}{*}{Fall} & \multirow{4}{*}{Day} & Standalone Edge & 166 & 14 & 15 & 0.915 & 0.922 & 0.918 \\  & & Standalone Server & 157 & 12 & 16 & 0.909 & 0.929 & 0.919 \\  & & Integrated edge–server (Ours) & 146 & 5 & 18 & 0.891 & 0.967 & 0.928 \\ \cline{1-1} \cline{2-8}  & \multirow{4}{*}{Night} & Standalone Edge & 83 & 7 & 8 & 0.917 & 0.919 & 0.918 \\  & & Standalone Server & 84 & 3 & 5 & 0.940 & 0.966 & 0.953 \\ \cline{1-1}  & & Integrated edge–server (Ours) & 79 & 5 & 0 & 0.997 & 0.941 & 0.968 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Day and night performance comparison: standalone edge and server vs. integrated edge–server architecture. detection criteria of actual anomalies may vary from environment to environment, and it is a great challenge to develop a model that can analyze various anomalies. In addition, the analysis of real-time CCTV video streaming may raise concerns about individuals' privacy and data security. The system proposed in this study may not provide a sufficient solution to these problems, which need to be addressed separately. In addition, most systems that utilize CCTV operate on a private network, and the proposed system must be applied as an addition to a control system that uses a private network. However, data encryption is essential in the communication between CCTV and edge devices, between edge devices and the ICS, and between the ICS and GPU servers, to protect the data from unauthorized access and prevent sensitive information from being leaked. In particular, the VSM of the ICS should ensure that the video data stored in the video database are accurately defined for the retention period and deleted and that faces and license plates that can be used to identify individuals in the video are de-identified as much as possible during storage to enhance individuals' privacy. Although the proposed system is focused on pedestrian abnormal behavior analysis in a large-scale CCTV environment, it is expected to be used in various domains that require control, such as traffic control, urban safety, and environmental monitoring similar to pedestrian abnormal behavior analysis, as well as in domains that require advanced analysis capabilities. For example, suppose there is a scenario in which analysis or data generation is performed on the smartphone itself. In that case, the results of analyzing or generating data through the models available on the smartphone are viewed. The user requests the server for higher-performance analysis or generation results, and the proposed system can contribute to efficiency and scalability in such a scenario. In this regard, the proposed system will have many possible domains to utilize, and if applied, it will be possible to create a scalable and efficient environment. In this study, we constructed and evaluated the performance of an edge-server-based pedestrian abnormal behavior analysis system. However, continued research and improvement efforts are required to enhance the system's performance further and increase its applicability in real-world environments.