# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 5 Conclusions and Prospects  
This study proposed a parking space detection method based on an AVM system and related vacancy analysis algorithms to facilitate indoor parking, where various directional lighting sources are present, and outdoor parking, where environmental variations can be extensive. The proposed method performed well in both settings. An AVM system, with its wider field of vision, was chosen as the sensor instead of traditional monocular cameras because the AVM system produces overhead images that look like they were shot from a camera mounted above the car roof. These images eliminate blind spots and provide clear views of nearby obstacles. This is the same reason why major car manufacturers began to invest in similar systems [29] and why these systems gradually became standard equipment in most cars. In addition, a highly robust analysis method was proposed to classify parking space occupancy. Two perspectives were used to detect features, classified as road or obstacle features, within the parking space. Road features were used to provide information regarding the possible vacancy of a parking space, and obstacle features were used to provide information regarding the possible occupancy of a parking space. Finally, these two types of information were integrated to determine whether a specific parking space is occupied. For road features, a region growing method was used to search for usable space within the parking space. For obstacle features, an edge detection method was used to determine whether a car is parked in the parking space. In indoor parking settings, we observed that the region growing method produced higher error rates than the edge detection method in detecting features. This was caused by dim lighting in indoor parking structures. Inadequ  
Figure 27: Examples of space occupancy classification failures. (**a**) Puddle; (**b**) Car distortion; (**c**) Motorcycle. lighting decreased the contrast between the road and cars and led to errors in region growing. In outdoor parking settings, particularly for roadside parking, the edge detection method produced higher error rates than the region growing method. This was because a vacant parking space may contain distracting objects such as manhole covers or storm drain covers. These distractors were easily mistaken for a car in the parking space. These results showed that road features and obstacle features each had their advantages in different settings. Thus, both types of features were included to offset the inadequacies of using only one type of features. Compared to traditional image-based methods of analyzing parking space occupancy, the proposed method demonstrated superior performance. In addition, with the exception of the two types of parking slot markings, the other slot markings can be classified into long T, short T, slanted, slanted long T, and slanted short T types. We also tested the proposed approach with the five types of parking slot markings (virtual images) representative of real conditions. The experimental results showed that our proposed approach can also successfully be used in these five types of parking lots. By integrating both road and obstacle feature analysis, this method improves upon traditional image-based methods of parking space analysis. The performance evaluation detailed in Section 4 showed that the method's detection rate was lower in indoor settings than outdoor settings because lighting problems are more severe in indoor settings than outdoor settings. However, the method achieved favorable detection performance overall. Furthermore, we tested and compared performance based on road features, obstacle features, and a combination of both. The results showed that integrating both types of features produced the lowest classification error rates. Future research into vacant parking space detection algorithms can consider one of three directions.