## 4 Experiments
### System Efficiency Analysis and Performance Comparison  
In the experiments, we continuously streamed the combined video created as described in Section 4.2 on 20 streaming servers to provide input to the system and measure the analysis time of the analysis modules used on the edge and server of the proposed system. This enabled us to obtain the processing speed on each device and check the GPU activation time in order to show that real-time processing is possible in the actual experimental environment. We also measured the performance of the edge-alone, server-alone, and edge-server coupling systems. Table 3 shows the time taken for each engine to process one frame on the edge and GPU server and the overall processing time and frames per second (fps) that can be processed. The edge is represented by both the Jetson NXavier and Jetson AGX Orin. Since the edge's assault and fall models require 20 fps for peak performance, the experimental criteria involve connecting one camera for the Jetson NXavier and two for the Jetson AGX Orin. On the other hand, the GPU server's analysis module requires 10 fps to achieve the best performance in the assault and fall model, and the GPU server's analysis module, which can process 21 frames, can process events from two CCTV cameras simultaneously in real time. The scalability of the proposed system in an experimental environment can be verified using (2) in Section 3.1. We can see that the GPU server's \(f_{\text{servercap}}\) is 21 and \(f_{\text{servercap}}\) is 10, as shown in Table 3. Moreover, given \(\lambda\), \(T\), and \(d\), we can determine the required GPU server's analysis module. For example, if we assume that the experimental time (\(T\)) is 1 h, 120 pedestrian anomaly events with an average duration of 15 s occur in each CCTV section in 1 h, and there are 20 such CCTV sections, we can predict that \(\lambda\) is 2400, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event** & **Dataset** & **Number of Videos** & **Video Length** \\ \hline \multirow{4}{*}{Assault} & AI Hub CCTV Abnormal Behavior [26] & 2780 & 235 h \\  & AI Hub Subway [27] & 6510 & 98 h \\  & AI Hub Residential [28] & 3579 & 95 h \\  & RWF-2000 [29] & 2000 & 3 h \\ \hline \multirow{3}{*}{Fall} & VFP290K [30] & 178 & 2 h \\  & URFD [31] & 70 & 0.1 h \\ \cline{1-1}  & AI Hub Abnormal Behavior CCTV [26] & 912 & 84 h \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Video dataset information related to assault and fall events. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Dataset**} & **AI Hub** & **AI Hub** & **AI Hub** & **WF2000** & **VFP290K** & **URFD** & **Combined** \\  & **Abnormal** & **Subway** & **Residential** & **[28]** & **[29]** & **[30]** & **[31]** & **Video** \\  & **CCTV [26]** & **[27]** & **[28]** & & & & \\ \hline \hline Number & & & & & & & \\ of videos & 190 & 90 & 90 & 100 & 100 & 70 & 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset information for combined videos. \(T\) is 3600, and \(d\) is 15, so approximately five analysis modules are required. Figure 8 shows the activation of the six server analytics modules loaded on three GPU servers when processing 20 video streams. The experiment involved 120 pedestrian anomaly events, each averaging approximately 15 seconds, over the course of an hour, based on the assumptions outlined above. Each GPU server has two GPUs, so two analytics modules are loaded for six. When an assault or fall occurs due to the edge's pedestrian anomaly analysis, an analysis request is sent to the GPU server's analysis module, and the server analysis module that received the analysis request is activated. When the analysis is completed, the server analysis module is deactivated. The activation status of the six server modules in the graph shows that each module is not always active, has idle periods, and is shut down in real time without exceeding the video streaming time when running for one hour. Thus, we can see that 20 CCTV cameras can be covered by 15 edge devices and 3 GPU servers.