# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 1 Introduction  
In traffic-congested cities, parking can be extremely challenging. Firstly, finding a vacant parking space may require considerable time. After finding a space, the driver's parking skill is tested. An unskilled driver can cause traffic congestion on busy streets or, worse, accidentally collide with the parked cars adjacent to the vacant spot. Because of this situation, automated parking systems became popular driver assistance systems in recent years [1,2]. Most automated parking systems available on the market are semiautomated systems based on ultrasonic sensors. Multiple ultrasonic sensors mounted on the vehicle provide two-dimensional scanning and positioning of a parking space and nearby obstacles. After detecting relative positions of the space and obstacles, the system uses geometric parameters to plot a path for parking and displays a virtual track for this path. The system then controls the steering wheel to assist the driver with parking in a parking space, but the driver must manually shift gears and apply brakes as necessary. However, ultrasonic sensor-based parkingassistance systems are highly dependent on the positions of adjacent vehicles. If the parking assistance system uses a reference vehicle that is parked improperly, the car with the parking assistance system will likely be parked improperly (Figure 1). Therefore, human intervention is often required to ensure the car is properly parked within a parking space. Furthermore, the parking assistance system cannot function if no vehicles are parked next to the desired parking space. Therefore, we hope to develop a parking system in the future that is truly and fully automated by using image sensors to detect the marked boundaries of a parking space and ultrasonic sensors to classify occupancy. The integration of multiple types of sensors will increase the robustness of the overall system. In this paper, we propose a parking space detection method based on an around view monitoring (AVM) system and related vacancy analysis algorithms. The images from AVM systems are primarily produced when individual images from four wide-angle lenses are converted into bird's-eye view images and then merged. The advantages of an AVM system are its wider visual range compared to monocular cameras and its ability to help drivers identify surrounding parking space markings and obstacles. The vacancy or occupancy of parking spaces was detected solely through AVM image sensors. The paper provides two contributions. Firstly, it presents a high-level fusion of parking space detection and related vacancy analysis to facilitate indoor parking, where inadequate light sources are present, and outdoor parking, where environmental variations can be extensive. The proposed approach performed well in both settings. Secondly, it proposes a space occupancy classification via a low-level fusion of road and obstacle features. 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. Finally, features were output to a naive Bayes (NB) classifier to classify the current status of the parking space. Section 1 provides a brief introduction to the research motivations, and an overview of the development progress of the present study is provided as follows: Section 2 contains an in-depth discussion of previous studies relevant to vacant parking space detection and includes an analysis of the advantages and disadvantages of the sensors and techniques used in the present study. Section 3 introduces the algorithm to detect vacant parking spaces and the methods used in each stage of the process.