# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 4 Experimental Results
### Experimental Process  
The experimental process was divided into two stages: parking space detection and space occupancy classification. In the parking space detection stage, the FAST corner detector was used to extract features. To reduce outliers and processing load, we designated a \(700\times 100\)-pixel region of interest to limit the detection field. Next, a circle was defined by choosing a detected corner as the center and by setting the radius to one-half of the marking line width (five pixels). The circular intensity proportion of this circle and the maximum vertical intensity profile in the \(x\)-axis were used to classify features. Because the set of classified corners can include outliers, we used RANSAC to estimate the model with the best fit; this model indicates the lane boundary line. We set the number of iterations to 50 in consideration of the small number of feature points. In addition, we defined the  
Figure 20: Around view monitoring (AVM) lens locations.  
criterion that the detected lane boundary line must be within 10\({}^{\circ}\) of the direction of travel to improve the accuracy of the lane boundary line. Divider lines must connect to the lane boundary line. Using this characteristic, we assumed a virtual lane boundary line, scanned 20 pixels to the right along the \(x\)-axis, and recorded accumulated pixels along the \(y\)-axis. Accumulated pixels under the threshold (15 pixels) were filtered out, and the width of the marking lines (10 pixels) was used to further eliminate errors in detection. After doing so, the type of parking space could be determined by observing the distance between peaks. After identifying the type of parking space, we obtained three samples perpendicular to the lane boundary line using criteria based on widths of the marking lines and the parking space. Finally, the least squares method was used to fit the three points into a divider line. This concluded the parking space detection stage.  
In the space occupancy classification stage, we viewed each parking space as occupying a grid in a grid and used road and obstacle features to determine whether the parking space was vacant. Because the texture of the road is homogeneous, we used region growing to search for road information. Because the seed points must accurately record the road, we chose locations near two corner features of the parking space. Standard deviation was used as the growth characteristic. The road feature was defined as the ratio of region growing area to total area. For the obstacle analysis, we observed that cars have complex structures and, therefore, must have a high number of edges. Therefore, we chose to use Canny's edge detector and defined the number of edges as the obstacle feature. Because the occupancy or vacancy of a parking space is a simple binary problem, we chose the less complex NB classifier for classification. Firstly, we needed to calculate the a priori probability of the existing data. The a priori probabilities of an occupied space and a vacant space are 0.5 each. Next, we individually calculated the probability that the features indicate an occupied or vacant slot; high-probability classifications are regarded as classified results. The entire experimental process and framework is depicted in Figure 21.