# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 2 Literature Review
### The Analysis Stage
#### 2.2.1 Parking Space Detection-Based Technology  
Image-based parking space detection is intuitively associated with line detection. For example, [PERSON] et al. [6] used the Hough transform on bird's-eye view images from monocular cameras to detect line segments through a combination of Hough space and marking line width restrictions. However, this method is unsuitable for AVM systems because AVM images are produced by the merging of four bird's-eye view images. This merging causes an inevitable curvature in line segments near the spliced boundaries. [PERSON] et al. [12] used the probabilistic Hough transform to detect line segments and inferred the parking space. The detection rate of the approach depends on the robustness of the line detector. In addition, some divider lines are blocked by previously parked cars, leaving only a short visible segment. These two commonly seen variations in line segments can both affect line detection in the Hough space.  
To resolve these problems, [PERSON] et al. [8; 13] used directional density-based spatial clustering of applications with noise to cluster line segments with different orientations. The type of parking space is identified using the geometric features of various types of parking spaces. However, because the clustered line segments lack parking space information, this method often mistakenly detects road markings. [PERSON] et al. [14] divided lane boundary lines into a pair of parallel lines with opposite gradient directions. Next, random sample consensus (RANSAC) was used to detect lane boundary lines. To detect divider lines between parking spaces, they used a distance transformation in combination with template matching to identify lines that could be divider lines. However, their method included only a single template; thus, it could be applied only to rectangular parking spaces. In addition to linedetection, others used corners as the feature for parking space detection. [PERSON] and [PERSON] [8] proposed applying the concept of hierarchical tree diagrams to parking space detection by using hierarchies to represent different types of parking spaces. Corner detection is the lowest level of the hierarchy; gradual progression up the hierarchy results in the identification of a corresponding parking space. [PERSON] and [PERSON] [15,16] used the Harris corner detector to extract junction patterns to infer the valid parking space. However, the overdependence on corner features can result in an inability to construct parking spaces because corners cannot be detected in dim indoor environments. For leaning-based parking space detection, [PERSON] et al. [17,18] proposed a machine learning approach in the field of parking space detection. They adopted boosting decision trees to train a marking-point detector to infer the parking space. In addition, [PERSON] et al. [19] proposed a deep convolutional neural network (DCNN) to formulate the parking space detection problem as two sub-problems--local image pattern classification and marking-point detection--to efficiently detect parking spaces.