A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Abstract

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

“…Both detection branches can detect multidirectional license plates by regressing the four corners of the license plate. After tilt correction, we can improve the license plate recognition performance [ 19 , 20 , 21 , 22 ]. The license plate information can be applied to barrier access control [ 20 , 22 ], vehicle target detection [ 49 ], vehicle re-identification [ 50 ], etc.…”

“…After tilt correction, we can improve the license plate recognition performance [ 19 , 20 , 21 , 22 ]. The license plate information can be applied to barrier access control [ 20 , 22 ], vehicle target detection [ 49 ], vehicle re-identification [ 50 ], etc. Moreover, the location of the license plate can be used for vehicle trajectory prediction [ 51 ] via license plate detection and tracking.…”

“…Moreover, many methods [ 6 , 9 , 10 , 11 , 16 , 18 ] do not consider the orientation of the license plate, which is only applicable to specific scenarios, such as parking charges and vehicle access/exit management. When it comes to more complex scenarios, such as road scenes, if we regard the tilted license plate as the horizontal direction, it may cause errors in the subsequent license plate recognition [ 19 , 20 , 21 , 22 ]. Although Dong et al [ 14 , 23 ] propose to detect the multidirectional license plate, these methods are very complicated due to adopting multiple separate models.…”

License Plate Image Analysis Empowered by Generative Adversarial Neural Networks (GANs)

“…To overcome the previous methods' drawbacks, Deep Convolutional Neural Networks (DCNNs) [21], [22] or Artificial Neural Networks (ANN) [23] present many hidden layers to learn high-level features in order to extend their capacity so that they can generalize not only the target re-identification function but also other computer vision problems, including image classification, object detection, VOLUME 10, 2022 semantic segmentation, and video tracking. As an example of using DCNNs in generalization for face detection and recognition, the raw input data is represented in the pixel matrix form, in which the pixels are abstracted.…”

Performance Analysis on Automatic Number Plate Recognition System and Methods

“…So all above matter solved by to make an algorithm which is used to detect vehicle and recognize its license plate through only videos which are captured by digital cameras. If we need any changes then only change the algorithm and modify its working [4]. That is why this technique is more reliable than the other hardware methods show in figure 2.…”