# A Systematic Review of Computer Vision and AI in Parking Space Allocation in a Seaport
## 3 Computer Vision and AI in Parking Space Allocation  
Several studies have explored the use of computer vision (CV) and artificial intelligence (AI) to manage parking spaces. [PERSON] et al. (2022) [PERSON] et al. (2022) proposed a prototype model that uses CV built on the Django framework in Python 3.0. The authors discussed various studies and emphasized how the use of CV could be advantageous in identifying occupancy in parking slots. According to their prototype, users can be informed about the available slots in the parking area for a selected time duration through video capturing using CCTV cameras. It's worth noting that their prototype is based on school and university parking areas. Another study by [PERSON] et al. (2020) [PERSON] et al. (2020) explored the feasibility of using edge computing and AI to identify space in the parking area. The prototype used real-time video feeds from CCTV cameras in the Angle Lake Parking Garage to allocate space in the garage. The authors achieved an accuracy of 95% in real-time scenarios. As previously mentioned, a driver in New York City spends approximately 107 h per year searching for a parking spot while driving, which becomes even worse during peak hours [PERSON] et al. (2021). Finding a parking spot manually in a multilevel parking garage is tough and leads to air pollution. Therefore, using AI and CV for the same is advantageous. The next question is which CV or AI technologies should be used for managing parking slots effectively. Researchers have suggested that technologies such as image processing and analysis, object detection and recognition, machine learning, deep learning algorithms, and real-time monitoring and control systems have been working effectively in space allotment in different parking areas. [PERSON] et al. (2021) [PERSON] et al. (2021) developed a smart parking system integrating image processing with AI and machine learning. The researchers identified various problems in a smart parking system and resolved them using AI and ML. Their model detects a vehicle at the entrance and enters the details of available slots. If there are no slots available, it will say 'Parking Full'. If there are slots available, the model will capture the number plate and open the entrance gate. On reaching the parking spot, the system will update itself with the date and time, and once the vehicle leaves, it will capture the date and time. Using machine learning, the authors calculate the parking charges, and once a payment is made, the entrance gate opens for the vehicle to leave. Similarly, [PERSON]. (2019) [PERSON]. (2019) used automatic license plate identification (ALPI) using image processing technology while designing a smart parking system. With the help of imaging processing and machine learning techniques, the authors successfully demonstrated how the model works. However, it was a pilot study, and scaling this study needs more details. [PERSON] et al. (2018) [PERSON] et al. (2018) built a smart parking system using image processing (reading number plates), AI, machine learning, big data, and neural networks. Ultrasonic sensors extract data on available parking spaces for parking a vehicle. Despite the advantages of using AI, CV, and machine learning in allocating space in parking areas, this system still has limitations. One limitation is the weather conditions when using sensors. The second is coverage. When using techniques such as AI and big data or machine learning, all data gets stored on the cloud, which must be strongly protected against hackers. A simple bug in the model could lead to complete model failure. The use of CV and AI technology has shown to be advantageous in properly managing parking lots. Researchers have developed several models that include image processing, machine learning, and deep learning algorithms to identify car occupancy, distribute parking spaces, and compute parking rates. Nevertheless, these systems have limits, such as weather conditions and data security concerns, that must be addressed.