Authors: Rutuja Gavai, muniba Ali, zarah Ali, Astha Gulhane
Abstract: The effective management of visitors has become a critical aspect of institutional security, smart campus initiatives, and organizational operations. Traditional visitor tracking methods, which rely on manual record-keeping or identity cards, are often prone to errors, delays, and inefficiencies. To address these shortcomings, vehicle plate recognition has emerged as a promising technology for developing intelligent visitor tracking systems. By leveraging the uniqueness of license plates as identifiers, organizations can implement automated, contactless, and reliable mechanisms to verify and monitor visitor entries and exits. This review paper presents a comprehensive survey of existing research on visitor tracking systems that integrate vehicle plate recognition. Key enabling technologies such as image preprocessing, Optical Character Recognition (OCR), fuzzy string matching, and cloud-based services (e.g., Microsoft Azure Cognitive Services) are analyzed for their role in improving accuracy and scalability. The study also discusses the integration of data analytics and reporting frameworks, which transform raw recognition results into actionable insights, such as visitor frequency patterns, identification of unknown vehicles, and predictive analytics for enhanced security planning. In synthesizing current literature, this review identifies major challenges, including image quality variations, diverse license plate formats, and real-time adaptability under unconstrained conditions. It also outlines research gaps in the application of deep learning, edge-based processing, and multimodal verification techniques for intelligent visitor management. The findings highlight that the combination of vehicle plate recognition with intelligent data-driven analysis offers a scalable and efficient pathway toward next-generation visitor tracking systems, particularly in academic institutions, corporate environments, and smart city infrastructures.
