# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 4 Experimental Results
### Experimental Results
#### 4.3.2 Outdoor Experimental Results  
Because outdoor settings contain more variables, we chose three different sites for the outdoor experiments: an outdoor parking lot, a parking lot under a bridge, and roadside parking spaces. Figure 23a shows the parking lot at Guang Hua Digital Plaza. Figure 23b shows the detection process. In this figure, the cars in first and third parking spaces are severely distorted. This is an imaging problem caused by the non-perpendicular angles between the cars and the camera. Regardless, the first and third parking spaces contained clear obstacle features such as tires or other parts of a car; therefore, they were classified as occupied. The second parking space exhibited features characteristic of a vacant parking space, i.e., high region growing ratio and low number of edges; thus, it was classified as vacant. Table 2 lists the quantified data for these results. Figure 23c shows the detected results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Header** & **Growing Ratio** & **Edges (Pixels)** & **Status** \\ \hline First slot & 0.894 & 44 & Vacant \\ \hline Second slot & 0.199 & 1556 & Occupied \\ \hline Third slot & 0.845 & 230 & Vacant \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parking space features at the National Taipei University of Technology. Figure 23: Experimental results in the Guang Hua Digital Plaza parking lot. (**a**) Experimental setting; (**b**) Detection process; (**c**) Detection results. Figure 22: Experimental results in the indoor parking lot at the National Taipei University of Technology. (**a**) Experimental setting; (**b**) Detection process; (**c**) Detection results. Figure 24a shows the parking lot underneath the overpass at Xinsheng North Road. Because the parking spaces are covered by the bridge above but open to sunlight on both sides, the problems caused by uneven lighting and shadows are rather severe. Figure 24b shows the detection process. In this figure, the first parking space is unevenly lit on the left and right sides. This decreased the contrast between road and obstacle features and caused erroneous region growth. However, a high number of edges were detected within the parking space; thus, it was classified as occupied. Because of the road stain in the second parking space, region growing did not successfully fill the entire parking space. However, too few edges were detected within the parking space, thereby rendering a classification of vacancy. The car in the third parking space was severely distorted and very few obstacle features were visible. However, with the assistance of road features, the space was correctly classified as occupied. Table 3 lists the quantified data for these results. Figure 24c shows the detected results. Figure 25a shows an urban roadside parking setting. The only option for outdoor parking, other than a parking lot, is roadside parallel parking. One key difference between perpendicular and parallel parking is that the AVM's field of vision can only detect one parking space at a time. Figure 25b shows the detection process when a parking space is occupied. Unlike the obstacle features of a perpendicular parking space, which contain the line structures of the front of the car and the license plate, the obstacle features of the parallel parking space primarily contain the line structures on the lateral side of the car and tires. The two types of parking spaces are similar in that an occupied parking space has a lower \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Header** & **Growing Ratio** & **Edges (Pixels)** & **Status** \\ \hline First slot & 0.394 & 1654 & Occupied \\ \hline Second slot & 0.908 & 0 & Vacant \\ \hline Third slot & 0.325 & 1325 & Occupied \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parking space features at Guang Hua Digital Plaza. Figure 24: Experimental results in the parking lot under Xinsheng North Road Overpass. (**a**) Experimental setting: (**b**) Detection process; (**c**) Detection results. region growing ratio and a higher number of edges.