Artificial Intelligence Techniques for the Recognition of Multi-Plate Multi-vehicle Tracking Systems: A Systematic Review

许可证 人工智能 计算机科学 执法 渲染(计算机图形) 计算机视觉 智能交通系统 计算机安全 工程类 运输工程 政治学 操作系统 法学
作者
Parneet Kaur,Yogesh Kumar,Surbhi Gupta
出处
期刊:Archives of Computational Methods in Engineering [Springer Science+Business Media]
卷期号:29 (7): 4897-4914 被引量:16
标识
DOI:10.1007/s11831-022-09753-4
摘要

In recent years, the number of vehicles has been rising enormously, which has caused difficulties in detecting a vehicle and the person involved in any criminal activity or traffic violation. With the advent of Artificial Intelligence, the Automatic License Plate Recognition System has been used for quite some time. automatic licence plate recognition (ALPR) is a photosensitive identification system that identifies the license numbers after effective detection from an acquired image dataset. The ALPR system also plays a significant role in law enforcement and rendering services in smart cities. It is an automated system that can identify a vehicle by recognizing the characters of the license plate. This system is fundamental to traffic control and managing criminal activities, intelligent parking systems, and various applications. Also, it provides a brief outline of various license plate detection and recognition techniques reported by researchers in India and other countries. The comparative analysis also highlights the work done in the direction of automatic license plate recognition by the different researchers using various learning techniques and results obtained by using the detection rate, accuracy, and localization. This study follows an investigational approach to analyze the character recognition techniques used in ALPR systems; research challenges the researchers face. This paper contributes an extensive review of the existing scenario of ALPR systems.
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