A Robust and Efficient Approach to License Plate Detection

Abstract

This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates from complex scenes in real time. A simple yet effective image downscaling method is first proposed to substantially accelerate license plate localization without sacrificing detection performance compared with that achieved using the original image. Furthermore, a novel line density filter approach is proposed to extract candidate regions, thereby significantly reducing the area to be analyzed for license plate localization. Moreover, a cascaded license plate classifier based on linear support vector machines using color saliency features is introduced to identify the true license plate from among the candidate regions. For performance evaluation, a data set consisting of 3977 images captured from diverse scenes under different conditions is also presented. Extensive experiments on the widely used Caltech license plate data set and our newly introduced data set demonstrate that the proposed approach substantially outperforms state-of-the-art methods in terms of both detection accuracy and run-time efficiency, increasing the detection ratio from 91.09% to 96.62% while decreasing the run time from 672 to 42 ms for processing an image with a resolution of 1082×728 . The executable code and our collected data set are publicly available.

DELP-DAR system for license plate detection and recognition

“…The images are captured from various locations and times, in different weather and illumination conditions. The second dataset is the PKU (Peking University) benchmark [8] which contains 3,977 images with Chinese LPs captured under diverse scenarios. It is categorized into five groups (G1-G5) corresponding to different configurations, environments, times, etc.…”

An overview of Various Techniques for Automatic License Plate Localization

“…Amit Kukreja et al [13], has proposed an efficient vehicle number plate detection method on multifaceted image of Indian automobile number plate using Sobel edge detection. Y. Yuan et al [14], has proposed a novel method to extract the candidate regions with less area to be computed by using line density filter and the true candidate region is identified using Cascade License Plate Classifier (CLPC) based on SVM with 96.62% detection rate on 3828 images. Abdulla et al [15], proposed a robust method using a large number of AdaBoost cascades with three levels pre-processing local binary patterns classifiers (3L-LBPs) under low quality image.…”

Detection of License Plate using Sliding Window, Histogram of Oriented Gradient, and Support Vector Machines Method

“…Until now, a number of techniques have been proposed to find the license plate of the desired vehicle through visual image processing. There are several algorithms used to determine the location of the vehicle number plate on the image, such as the Part-Based Model (PBM) method and the Histogram of Oriented Gradients (HOG) in [2], edge statistics and mathematical morphology in [3], vertical projection method in [4], Haar-like cascade classifier and edge in [5], the authors [6] using artificial neural networks (ANNs) method to determine plate location by scanning sliding window, the authors [7] and [8] determines the plate location using SVM linear classification, genetic algorithm (GA) and Geometric Relationship Matrix (GRM) in [9].…”