## 2 Related Work
### Surveillance Video Analysis System  
In recent years, CCTV video analytics systems have been widely used for security and surveillance, leveraging advanced computing technologies and deep learning algorithms. In particular, edge-based processing technologies are contributing to enhancing the safety and security of cities through their advanced computing capabilities; they overcome the limitations of centralized data processing and enable real-time data analysis. Recent research has focused on utilizing edge devices and applying artificial intelligence models to video analytics systems. For example, reference [6] proposes an anomaly recognition system for edge computing using Raspberry Pi-based edge devices. By preprocessing and analyzing video data at the network's edge, the system improves real-time responsiveness and re duces the burden of data transmission on the central server. Similarly, ref. [7] introduces NAVIBox, a real-time vehicle-pedestrian hazard prediction system. The system focuses on detecting and predicting potential collisions between pedestrians and vehicles in a real-time edge vision environment. In reference [1], a long short-term memory (LSTM) [8] model on an edge computing system is used to recognize and predict the appearance patterns of objects in surveillance videos. In particular, it applies optimization to improve the system's efficiency and real-time processing power. It proposes an efficient analysis method using edge devices, reducing the bandwidth use and improving the response time by processing the videos on edge devices and sending only the results to the cloud for final analysis. The aim of the research in [6; 7] is to embed advanced computing functions in edge devices to overcome the limitations of centralized data processing and enable real-time data analysis. The system proposed in this research uses edge devices capable of real-time data analysis to distribute the load concentrated on the analysis server by connecting edge devices and GPU servers. Another study that utilizes AI models in a centralized video analytics system is [9], which integrates AI into CCTV systems to comprehensively evaluate the effectiveness of intelligent video surveillance in community spaces. The study discusses the various benefits of integrating AI into CCTV systems, including a methodology for the development and integration of AI-based video analytics algorithms, case studies of real-world applications of AI-integrated CCTV systems in community spaces, a performance evaluation and analysis of AI-enhanced surveillance systems, and ethical and legal considerations related to AI-integrated surveillance, such as privacy and data security. Reference [10] explores the application of deep learning techniques to improve the performance of video surveillance systems in financial institutions such as banks. The work focuses on how deep learning technology can reduce security vulnerabilities and improve crime prevention and early detection. It introduces experimental studies to verify the performance of deep learning-based video surveillance systems in natural banking environments, as well as optimization methodologies for deep learning models to improve the performance of video surveillance systems. It highlights the potential for deep learning-based video surveillance systems to become the new standard for bank security and highlights the importance of continued research and technology development in this area. The system proposed in this paper performs the final analysis on the GPU server only on the CCTV videos that have been filtered through pre-analysis on the edge device, similar to the research in [9; 10], which applies AI models to a system that analyzes CCTV videos. However, recent research on CCTV video analytics systems has focused on real-time pedestrian anomaly detection using advanced computing technologies and deep learning algorithms.