## 2 Related Work
### Pedestrian Abnormal Behavior Detection in Surveillance Videos  
Pedestrian anomaly analysis plays an important role in urban safety and public security and is constantly evolving with advances in real-time detection and response techniques. This research uses advanced computing technologies and deep learning algorithms to detect and analyze real-time pedestrian anomalies. This research proposes a novel system that maximizes the efficiency and scalability of real-time analysis by linking edge devices and GPU servers. The system performs initial data processing at the edge and sends only filtered data to the GPU server, increasing the efficiency of data processing and minimizing the network load. In the proposed system, the pedestrian anomaly analysis is initially performed on the edge device and finally on the GPU-equipped server, so a pedestrian anomaly analysis method is required for application to the edge device, and a pedestrian anomaly analysis method must be applied to the GPU-equipped server. Therefore, in this section, we introduce related works that are relevant to the edge devices and GPU-equipped servers in the analysis system proposed in this study. First, there are three studies on pedestrian anomaly analysis methods that can be used on edge devices, i.e., refs. [11; 12; 13], both of which use the object detection results of the YOLOv7 [13] model and propose a method for the analysis of pedestrian anomalies in the rule base. Reference [11] proposes a real-time assault event detection method based on object tracking to detect assaults in CCTV images. Reference [12] focuses on the real-time detection of fall events. These two studies propose efficient algorithms and system structures for the real-time processing of CCTV videos based on object detection. Their efficiency and accuracy are verified through specific case studies that identify assaults and falls. Meanwhile, studies on pedestrian abnormal behavior analysis methods applicable to GPU-equipped servers are described in [14; 15; 16; 17]. Reference [14] proposes a lingering behavior detection method based on pedestrian activity area classification, which dynamically calculates pedestrian activity areas to classify lingering behavior into three categories and uses an algorithm that can accurately and robustly detect different types of lingering behavior without complex trajectory computation. The authors in [15] propose a 3D convolutional neural network-based method for the more accurate detection of anomalous behavior in sparsely populated environments. The study develops a novel analysis framework that can effectively identify anomalous behavior, even in relatively sparsely populated environments, by considering temporal and spatial features. Reference [16] focuses on violence detection based on spatial and temporal features. By utilizing 3D convolutional neural networks, we develop a methodology to detect and predict violent behavior in real time, especially in public spaces, which can contribute to the early detection of and response to violent incidents. Reference [17] explores the use of deep neural networks based on pose estimation to detect falls in human objects. The research focuses on developing high-performance algorithms to analyze human posture in real time and accurately identify abnormal movements such as falls. While these methods may perform well when processing a single video stream, without considering efficiency and scalability, they may not be appropriate for the processing of multiple video streams. Pedestrian abnormal behavior analysis methods applicable to edge devices can process one or more CCTV video streams, depending on the number of frames per second required for pedestrian abnormal behavior analysis and the number of frames per second that the edge device can process.