A DEEP LEARNING-POWERED SMART PARKING SYSTEM BASED ON FACIAL RECOGNITION AND LICENSE PLATE ANALYSIS
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.2647Keywords:
Smart Parking System, Face Recognition, License Plate-Based Verification, Features Extraction, Character RecognitionAbstract [English]
Parking congestion has become a major problem in today's metropolitan environments, resulting in lost time, higher emissions, and irritated drivers. There is a significant lack of parking spots in large cities as a result of the expanding number of vehicles and the ever-increasing metropolitan population. In addition to making it difficult and time-consuming to find a parking space, this shortage has made pollution and traffic congestion worse. Conventional parking management methods are becoming less and less effective in addressing these issues because they usually depend on physical barriers or manual ticketing. Innovative approaches that combine automation and technology are getting more and more popular as a way to solve this problem. Conventional parking management systems often rely on human interaction and physical infrastructure, which leads to restricted scalability and inefficiency. On the other hand, adding deep learning methods and cutting-edge computer vision technologies to parking management opens up new possibilities for a smarter, more effective, and more user-friendly experience. This system makes use of two essential elements: automatically recognizing license plate numbers for vehicle identification and facial recognition technology for person identification. By seamlessly integrating these technologies, the system not only facilitates effortless parking but also enhances security and optimizes parking space utilization. In this survey, we will explore the fundamental components, benefits, and potential impact of the Face and Number Plate-Based Smart Parking System. Experimental results shows that improved efficiency in smart parking system using face and number plate verification system.
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Copyright (c) 2024 M. Kannan, Aakash K, Haribalan S, Hariharan R, Jayabalan P
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