## 2 Related Works
Furthermore, Bosch Corporate Research introduced a real-time video surveillance system that prioritizes robustness in its design [17]. This paper also presented quality measures for a common data set named PETS. Moreover, a set of objective, scientific evaluation indexes for user requirements of highway video surveillance networking systems were studied in [18]. These indexes were utilized to evaluate the performance of a developed video surveillance networking system and optimize its efficiency for real-time surveillance. Finally, study [19] suggests that analyzing the performance of a surveillance camera network system with an image recognition server based on the frame discard rate and server utilization can help ensure the efficient operation of evolving surveillance camera networks. The resulting analysis will be valuable for optimizing camera network systems. Meanwhile, recent research has revealed that surveillance camera systems, such as CCTV, may not solely be employed for the purposes of preventing criminal activities, but they are also a crucial tool in resolving a multitude of issues, including traffic control, managing epidemics and disasters, and maintaining facilities. In [20], the proposed PSI-CNN architecture improves face recognition performance by extracting untrained feature maps across multiple image resolutions, allowing the network to learn scale-independent information and outperforming the VGG face model in terms of accuracy. Paper [21] introduces a real-time ITS that uses video imaging and deep learning to detect vehicles driving the wrong way. The system achieves a 91.98% accuracy in detection by combining YOLOv3, Kalman filtering, and an \"entry-exit\" algorithm. Study [22] uses CCTV cameras in embedded systems to monitor traffic. A lightweight CNN and the SORT algorithm enable real-time processing for vehicle detection and tracking. Labels are generated in an unsupervised manner, improving traffic estimation accuracy. In study [23],an AI-powered system uses CCTV cameras to monitor social distancing and identify high-risk zones for infection. The proposed system outperforms traditional methods in person detection and offers a risk assessment scheme. In [24], a new model is proposed that achieves better action recognition and real-time capability, making it suitable for low-cost GPU-based embedded systems. This study suggests an algorithm that uses a deep learning model to improve vehicle license plate recognition in CCTV images. A deep learning model was created for detecting road abnormalities and preventing accidents in [25]. It utilized FFmpeg to extract accident images and identified collision types using the YOLO algorithm. The service included a car accident and road obstacle detection model, warning notifications, and photo capture. The authors of [26] dealt with detecting weapon problems in CCTV surveillance videos. Here, advances in computer vision and object detection led to a real-time system using a high-performance GPU and optimized implementation for improved speed and accuracy. Digital twin technology can aid in disaster safety management of underground utility tunnels. The study in [27] compared two methods for estimating depth using a single CCTV camera: the coordinate system conversion method and the DenseDepth transfer learning method. The coordinate system conversion method was found to be more suitable for accurate depth estimation. Manually detecting sewer defects can be a time-consuming and ineffective process. Therefore, an accurate and automated detection method is necessary. In a previous study [28], the authors suggested the utilization of YOLOv4 with the SPP module and the DIoU loss function to enhance detection and classification accuracy.