## 4 Experiments
### Experimental Environment  
In this section, we describe the environment for the validation of the efficiency and scalability of the system proposed in this paper. Figure 4 shows the environment configured to experiment with the proposed system. For the experiments, the CCTV environment was configured with 20 video streams using 20 RTSP video streaming servers. The videos streamed using the video streaming servers were generated by stitching videos from multiple datasets and labeling the regions in which events occurred; this is described in detail in Section 4.1. The edge environment was configured using 10 Jetson Xavier NX-based edge devices and 5 Jetson AGX Orin-based edge devices, while the ICS used a single server with a CPU i9-10940X, with 256 GB RAM, 1 TB SSD, and 20 TB HDD. The GPU server was configured using three servers with a CPU i9-10940X, with 256 GB of RAM, 1 TB of SSD, and RTX 3090 \(\times\) 2. The analytics modules on both the edge devices and GPU servers analyze assault events, which can occur between pedestrians during pedestrian anomalies, and fall events, which indicate when a pedestrian has fallen or fainted. A total of 10 Jetson Xavier NX-based edge devices were used with 1:1 mapping with CCTV, and 5 Jetson AGX Orin-based edge devices were used with 1:2 mapping with CCTV. The analytics module of the edge devices performs pedestrian abnormal behavior analysis through object detection, two types of object tracking, and two pedestrian abnormal behavior detection engines. Object detection uses YOLOv7-tiny, the lightest model in the deep learning-based YOLOv7 [13] family. The YOLOv7-tiny model is trained on CCTV video datasets based on the learning method in [18; 19] and optimized with FP16 lightweight and TensorRT [20] conversion. Assault and fall event detection for pedestrian safety leverages the assault analysis engine from [11] and the fall analysis engine from [12], respectively. Both pedestrian event analysis engines are rule-based and can be processed in real time. To meet the requirements of different object tracking models for pedestrian anomaly analysis, the ByteTrack [21] model is used in [11] and the SORT [22] model is used in [12]. The development environment for the edge device includes JetPack 4.5, Python 3.6, DeepStream SDK 5.1 [23], and TensorRT 8.4, utilizing the YOLOv7 TensorRT model. When an edge device connects, the ICS's VSM creates threadsusing Celery to receive and store the stream in the video database. These threads are responsible for receiving and storing continuous streams from the edge devices. When a particular edge device is removed from the connection list, the associated threads are also terminated to manage the resources efficiently. The ABER scans for pedestrian anomaly events from the edge devices in a standby state. When an event occurs, it requests the GPU server to analyze the video from the CCTV at the specified period via the SATS. A message queuing system utilizing Celery, Redis, and RabbitMQ allows real-time events to be queued and processed sequentially to maximize the system's processing power. The GPU server only performs analysis when an assault or fall event is detected, and notifications are sent to the ICS's RND module when the analysis is complete. When the GPU server receives an analysis request, it requests the video from the ICS's VSM using the CCTV information and time information included in the analysis request, receives the video, and starts analyzing it for assaults and falls. The model of [24] is used for assault analysis. The model of [17] is used for fall analysis. Figures 5 and 6 show the structures of the pedestrian anomaly analysis engines of [17; 24]. For the assault detection model, the pose estimation results for each frame are used to generate pose joint images and stacked by \(T\) to determine the presence of an assault through the network shown in Figure 5.  
Figure 4: Configuration of the experimental environment for the efficiency and scalability evaluation of the proposed system.