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 SVMs using color saliency features is introduced to identify the true license plate from among the candidate regions. For performance evaluation, a dataset consisting of 3828 images

captured from diverse scenes under different conditions is also presented. Extensive experiments on the widely used Caltech license plate dataset and our newly introduced dataset 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 ms to 42 ms for processing an image with a resolution of 1082*728. The executable code and our collected dataset are publicly available.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• They proposed a novel scheme to automatically locate license plates by principal visual word (PVW), discovery and local feature matching.

• Observing that characters in different license plates are duplicates of each other, we bring in the idea of using the bag-of-words (BoW) model popularly applied in partial-duplicate image search.

• Unlike the classic BoW model, for each plate character, we automatically discover the PVW characterized with geometric context.

• Given a new image, the license plates are extracted by matching local features with PVW. 

DISADVANTAGES OF EXISTING SYSTEM:

• Most of the previous methods perform well only under certain predefined conditions.

• Some common restrictions include fixed illumination, license plates with little blur or distortion from viewpoint changes, relatively simple backgrounds and the presence of only a single license plate in an image.

• More recent state-of-the-art approaches impose fewer restrictions on license plate detection at the cost of increased computational complexity.

• However, these approaches still have difficulty extracting license plates from complex scenes.

PROPOSED SYSTEM:

This paper develops an efficient and robust approach to license plate detection that is able to accurately localize one or multiple vehicle license plate(s) with diverse variations from complex backgrounds in real time. To speed up the detection algorithm overall, we first investigate how to reduce the size of the original high resolution image without decreasing license plate detection performance. Note that because of the negative effects that are generally introduced by the downsampling method that is commonly used in image processing, most previously developed methods perform license plate detection using the original image. Then, we analyze the common characteristics among diverse license plates and their major differences with respect to background regions to serve as a basis for designing a region filter to exclude irrelevant regions in the image. Furthermore, we study which features are most discriminative for license plate detection and then propose an efficient and robust classifier to ultimately localize the exact position of the license plate in the image.

In summary, the contributions of this paper are as follows:

1) A novel line density filter (LDF) is proposed to extract candidate license plate regions, thereby significantly reducing the area to be analyzed for license plate localization.

2) An efficient license plate verification method is proposed to accurately detect the true license plate from among the candidate regions using a cascaded license plate classifier (CLPC), which is trained based on color saliency features.

3) For performance evaluation, we present a newly collected challenging dataset that consists of 3828 license plate images with variations in illumination, license plate appearance, vehicle location and weather conditions.

4) We demonstrate that the proposed approach outperforms state-of-the-art methods by a large margin in terms of both detection accuracy and run-time efficiency.

ADVANTAGES OF PROPOSED SYSTEM:

• Our approach is the fastest, requiring only 42 ms for license plate detection.

• Our proposed method increasing the detection ratio from 91.09% to 96.62% while decreasing the run time from 672 ms to 42 ms for the processing of an image with a resolution of 1082×728.

MODULES:

• Preprocessing
• Candidate extraction
• Candidate Verification

MODULES DESCSRIPTION:

Preprocessing

The original color images for license plate detection and recognition are generally captured at a high resolution, which ensures that the small license plates and the even smaller characters on them can be processed and recognized using computer vision algorithms. However, this high resolution also imposes a high computational cost for detecting the license plate in an image. To address this issue, one suggestion might be to downscale the input image for license plate detection. In this module, we exploit bilinear interpolation for image downscaling, in which each output pixel value is computed as a weighted average of the nearest pixels

Candidate extraction

In this module, we develop a novel scheme for extracting license plate candidates. The candidate extraction method consists of edge detection, edge image inarization via adaptive thresholding (AT) and the proposed novel line density filter. An extension of the Sobel operator is used to detect the boundaries of objects in the image. Then, AT is exploited to eliminate weak edges and generate a binary edge image. Finally, a line density filter (LDF) method is proposed to connect the high-density regions in the binary edge image along the horizontal and vertical directions. Several examples of the results generated by each component of the candidate detection method.

Candidate Verification

Once the candidate regions have been obtained using the LDF method, we need to further distinguish the true license plate region(s) from other regions. We use a two-step approach to verify these candidate regions. First, connected-component labeling (CCL) is applied to find candidates and remove areas that obviously do not exhibit the geometrical characteristics of a license plate. The final step of LPD is to identify the real license plates from among the detected candidate regions Considering that a license plate usually consists of two dominant colors, we propose a cascaded license plate classifier (CLPC) based on linear SVMs using color saliency features to verify license plate candidates. The feature vector is extracted from both the HSV and RGB color spaces to ensure the effectiveness of the CLPC.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System            : Pentium Dual Core.

• Hard Disk       : 120 GB.

• Monitor          : 15’’ LED

• Input Devices : Keyboard, Mouse

• Ram                : 1GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.

• Coding Language : MATLAB

• Tool                      : MATLAB R2013A

REFERENCE:

Yule Yuan, Member, IEEE, Wenbin Zou, Yong Zhao, Xinan Wang, Xuefeng Hu, and Nikos Komodakis, “A Robust and Efficient Approach to License Plate Detection”, IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017.