# Autonomous Driving Assistance with Dynamic Objects Using Traffic Surveillance Cameras
## 2 Identification of Dynamics Objects  
Recently, with the development of artificial intelligence (AI) technology, technologies that can identify and track target objects based on images in various devices have been developed. For this reason, the traffic surveillance camera, an image sensor incorporating AI technology, can be used for a variety of purposes in many fields. This goes beyond a simple monitoring role that can be checked with the naked eye, and can serve as an important sensor that can provide relevant information by analyzing road conditions in real time in terms of road infrastructure. In addition, for the scalability of monitoring system connection, which is the purpose of traffic surveillance camera utilization, related functions were implemented along with modular-based systems, and Figure 1 shows multiple traffic surveillance cameras in a certain area. It is helpful in understanding traffic flow on a road as well as for obtaining information on blind spots at an intersection. Traffic surveillance camera images are used as multiple inputs, dynamic objects (vehicles, people, bicycles, etc.) included in the image frame are identified using AI, and the location is tracked by creating a minimum bounding box of the object. For the identified and tracked object, the position defined in the image coordinate system is transformed into the map coordinate system through a transformation matrix in which the coordinate system transformation relationship between the camera coordinate system and the map is established and also configured for multi-user support. It performs the process of uploading object-related information to the server in real time, and uses the JSON format for the standardization of information delivery [17]. The object information delivered in this way is visualized using precision, aerial, and general maps through a web-based visualization module designed to support various devices, as shown as Figure 2.  
In order to accurately identify and track the target object in an image, information such as the shape and color of the object can be used. Therefore, the process of recognizing one object in one image requires a large amount of effort and time before AI technology can support high-level functions. However, with the advent of artificial intelligence technology, a technology for recognizing multiple objects in one image at a near real-time speed is in development. As these technologies are used in various fields, they are gradually becoming more advanced. Figure 1: Example images of multiple traffic surveillance cameras. Figure 2: Overview of the implemented system. Image processing technology, including object detection, has rapidly improved recently [18]. It is utilized in autonomous driving with various algorithms such as Faster Region-based Convolutional Neural Networks (R-CNN) [19], You Only Look Once (YOLO) [20], and Single Shot Detector (SSD) [21]. Faster R-CNN is an improved model with the aim to achieve a real-time processing speed using Fast R-CNN from the initial R-CNN. Although the structure is similar to that of Fast R-CNN, it uses a regression network called a very small region proposal network for selective search, showing a processing speed performance of 250 times that of R-CNN and 25 times that of Fast R-CNN. YOLO is a method that tracks objects by dividing them into grid units corresponding to n boxes rather than pixel units of an image, and is a model suitable for real-time detection systems with a near real-time processing performance. SSD is a model that has a balanced accuracy and processing speed by tracking many target objects in one image based on feature maps of various sizes. Therefore, we adopted the YOLO algorithm, which is known for its fast object identification time. The YOLO model for training used the YOLOv4 architecture. In addition, the recently announced YOLOv4 shows a considerable improvement in performance [22; 23]. In order to use such a model, learning of the target object to be identified and tracked should be performed.