A Review of Parking Slot Types and their Detection Techniques for Smart Cities

Abstract

Smart parking system plays a critical role in the overall development of the cities. The capability to precisely detect an open parking space nearby is necessary for autonomous vehicle parking for smart cities. Finding parking spaces is a big issue in big cities. Many of the existing parking guidance systems use fixed IoT sensors or cameras that are unable to offer information from the perspective of the driver. Accurately locating parking spaces can be difficult since they come in a range of sizes and colors that are blocked by objects that seem different depending on the environmental lighting. There are numerous auto industry players engaged in the advanced testing of driverless cars. A vacant parking space must be found, and the car must be directed to park there in order for the operation to succeed. The machine learning-based algorithms created to locate parking spaces and techniques and methods utilizing dashcams and fish-eye cameras are reviewed in this study. In response to the increase in dashcams, neural network-based techniques are created for identifying open parking spaces in dashcam videos. The paper proposed the review of the existing parking slot types and their detection techniques. The review will highlight the importance and scope of a smart parking system for smart cities.

Keywords

• smart cities
• smart parking
• machine learning
• dashcams
• shuttle parking system
• fish-eye