# Autonomous Driving Assistance with Dynamic Objects Using Traffic Surveillance Cameras
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Table Name** & **Type** & **Type** \\ \hline \multirow{4}{*}{A} & Lane marking & Line & Left and right lanes, lane color, centerline, \\  & & & bus-only lane \\ \cline{2-4}  & Road facilities & Line & Signal stop line, cross walk stop line \\ \cline{2-4}  & Centerline of lane & Line & Highway, national highway, local road, city road, \\  & & & country road \\ \hline \multirow{4}{*}{B} & Sign facilities & Point & Attention, regulation, indication subsidiary sign \\ \cline{2-4}  & Signal & Point & Signal \\ \cline{2-4}  & Road direction & Point & Road marking, direction marking \\ \cline{2-4}  & Guide line & Line & Guide line for left turn \\ \cline{2-4}  & Crosswalk & Polygon & No stopping zone, guide zone, uphill \\ \cline{2-4}  & & & road, crosswalk \\ \cline{2-4}  & No-autonomous driving zone & Polygon & Protection zones (children, disabled elderly, and others) \\ \cline{2-4}  & Roadside facilities & Point & Roadside facilities such as streetlamps and road signs \\ \hline C & Node & Point & Node \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data model of high-definition map. Related information by tracing a vehicle with a traffic surveillance camera image and visualizing an event information map by linking the tracking results with Google Map through a transformation relationship is provided [13]. In addition, a method of object visualization from a top view was proposed with object tracking and a coordinate transformation in a specific area through the transformation relationship between six cameras employing the stereo vision method [14]. A fusion method using an image sensor, GPS, and LiDAR was proposed to estimate the location of a target object by tracking the location of the object [15]. In the case of related existing studies, various approaches for the use of traffic surveillance cameras have been presented from a monitoring point of view [16]. However, since the purpose of providing information is focused only on semi-real-time image analysis, it has disadvantages such as poor usability other than monitoring or requiring multiple sensors. There are only a few approaches that can be used to match coordinates between 2D images and 3D real roads because they are difficult to measure. In this study, we suggest a transformation method with multiple traffic surveillance camera images and HD maps and an evaluation method as a way to increase the utilization of established traffic surveillance cameras. The main contribution of this paper can be summarised by the following points. First, we proposed a fusion method to evaluate accurate vehicle positions on the road. Additionally, a high-precision localization approach was used with a combination of traffic surveillance camera equipment and HD maps. Secondly, we proposed a simple method to unify heterogeneous coordinates. Furthermore, moving objects (vehicles, people, etc.) were identified and tracked in traffic surveillance camera images and information related to their location was provided as spatial information on the map. We proposed a method that can be used in various environments. To this end, the moving object location information of the image was converted to the left position on the map, and the object was visualized as spatial information using precision, aerial, and general maps through a web-based visualization module. This paper is organized as follows. The identification and tracking of dynamics objects are introduced in Section 2. In Section 3, an overview of the proposed localization method and a detailed introduction of the basic theoretical method are provided. In Section 4, the factors that affect the accuracy of estimation based on a real environment are analyzed. In Section 5, an evaluation and discussion are presented, along with the conclusion and indications of future research efforts.