## 5 Discussion  
Through the experiments described in this paper, we demonstrate that the proposed system with an edge-server structure is more scalable and efficient for pedestrian abnormal behavior detection compared to the edge and server alone. However, during the research process, we needed assistance in collecting and processing the data under various conditions similar to natural urban environments. In particular, the video resolution of the published datasets was low, such as \(320\times 240\) or \(640\times 360\). In CCTV images, human objects occupy less than \(1/10\) of the area in the frame, so high-resolution videos are required to improve the accuracy of pedestrian abnormal behavior analysis. It was time-consuming to create combined videos, including standard videos with no events other than assaults and falls, and to label the times at which the events occurred. In addition, to test real-time streaming data processing, it was challenging to design and implement a streaming environment with 20 streams and to ensure seamless data communication and processing between the edge and ICS and the ICS and GPU server. In particular, much effort was required to develop a mechanism to transmit the data without network delays or data loss and promptly deliver the analysis results. The limited computing power of edge devices complicates the running of complex analytics algorithms that require high accuracy, so much experimentation was needed to select the available models. The limitations of the proposed system include the fact that we used the combined videos generated in this study for video streaming. Although the combined videos consisted of publicly available data, they were specific to the experimental environment, which may limit their generalizability to various real-world environments. In particular, this study targeted pedestrian anomalies such as assaults and falls. However, the definition and \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Abnormal Behavior**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Number of Units**} & \multicolumn{2}{c}{**Video Length**} & \multicolumn{2}{c}{**Proc.**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**FN**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1**} \\  & & & & **Length** & & & & & & & \\  & & **\(\times\) CCTV** & & **(s)** & & & & & & \\ \hline \multirow{4}{*}{Assault} & Standalone Edge & 18 Edge & \(313\times 20\) & 294 & 288 & 70 & 32 & 0.899 & 0.803 & 0.848 \\  & Standalone Server & 3 Server & \(313\times 20\) & 1040 & 297 & 51 & 23 & 0.929 & 0.853 & 0.889 \\  & Integrated edge–server (Ours) & 18 Edge & \(313\times 20\) & 314 & 286 & 40 & 21 & 0.932 & 0.877 & 0.904 \\ \hline \multirow{4}{*}{Fall} & Standalone Edge & 18 Edge & \(313\times 20\) & 294 & 249 & 21 & 23 & 0.915 & 0.921 & 0.918 \\  & Standalone Server & 3 Server & \(313\times 20\) & 1040 & 241 & 15 & 21 & 0.906 & 0.941 & 0.923 \\ \cline{1-1}  & Integrated edge–server (Ours) & 18 Edge & \(313\times 20\) & 314 & 225 & 10 & 18 & 0.926 & 0.958 & 0.942 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison: standalone edge and server vs. integrated edge–server architecture.