# Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection
## 2 Related Works  
Automatic parking systems using AVM images can be divided into a parking slot detection part and a path planning and tracking part.  
Various studies have been conducted on the detection of parking slots using AVM-based systems. To recognize parking lines, a commonly used method involves locating a corner point through line recognition and determining whether or not that corner is part of a parking dividing line. For instance, parking lines have been detected in AVM images using the Hough transform [8]; a top hat filter and directional DBSCAN [9]; Canny edge detection and the Radon transform [10]; local symmetry of brightness and a Dirac comb [11]; the Sobel filter, RANSAC, and edge pairs [12]; FAST corner detection and RANSAC [13]; and a probabilistic occupancy filter [14]. Moreover, [PERSON] et al. detected the corners of parking lines and parking slots using gradient values, AdaBoost, and decision trees [15]. [PERSON] et al. recognized parking slots by detecting lines and junctions [16], and parking slots have also been detected using a semantic segmentation filter and a convolutional neural network (CNN) [17]. [PERSON] et al. recognized parking areas using a deep CNN [18]. Partially  
Figure 1: Proposed automatic parking system using AVM images.  
occluded parking lines were recognized by [PERSON] et al. using segment labeling [19] and by [PERSON] et al. using line features and feature clustering in the top view image [20].  
Furthermore, various studies have been conducted on path planning and tracking. [PERSON] et al. defined perpendicular, angle, and parallel parking modes, and created parking paths using a fast motion planning algorithm [21]. Subsequently, they created optimized paths that can be used for narrow spaces [22]. [PERSON] et al. defined perpendicular parking and parallel parking modes and created parking paths using a four-phase algorithm [23]. [PERSON] et al. and [PERSON] et al. generated paths for perpendicular parking using a clothoid-based method [24; 25]. [PERSON] et al. created a parallel parking path using clothoids and performed parking control using an actual vehicle [26]. [PERSON] created circular trajectories using the parking lines recognized by the AVM system and performed automatic parking with a test vehicle [10]. [PERSON] et al. created paths for perpendicular parking and parallel parking using fuzzy control and performed parking control using a mobile robot [27]. Parking paths have been created using collision-free and nonholonomic paths generated by the slice projection technique [28] and a method that considers the surrounding environment and generates an optimal path by dividing the motion of a car into translation and rotation motions [29].  
The proposed method uses the Hough transform to detect lines and cross points. Using the Hough transform has the advantage of detecting straight lines and cross points with simple operations. On the other hand, there is a disadvantage that it is difficult to distinguish the parking line from other lines. Therefore, the proposed method uses CNN to distinguish cross points with high accuracy. Furthermore, the proposed method uses circles and lines for path planning. Although this method has the disadvantage of taking a long time to control the vehicle, it has the advantage of being suitable for application to an actual vehicle and controlling the vehicle with high accuracy. Therefore, the proposed method uses a method using a circle and a line that is suitable for high accuracy and real automobile applications.