scholarly journals EXPLOITABLE EDGE ANALYSIS FOR FREE FLOW VEHICLE PLATE LOCALIZATION

2021 ◽  
Vol 10 (01) ◽  
pp. 27-42
Author(s):  
Abbas Salimi Zaini ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Azizi Abdullah ◽  
Nelson Budin Sana ◽  
Shariffpudin Basiron
Keyword(s):  
Recognition Performance ◽  
Region Of Interest ◽  
Recognition System ◽  
Free Flow ◽  
License Plate ◽  
Vertical Projection ◽  
Data Set ◽  
Edge Information ◽  
Vehicle Recognition ◽  
Toll Collection

In Malaysia, vehicle recognition system (VRS) such as vehicle plate recognition and counting, is rapidly growing and applied in many areas such as to identify vehicle identities for the law enforcement by authorities and electronic toll collection by highway agencies. Uncovering the region of interest in chaotic illumination environment at free flow road makes localizing license plate is critical process of VRS. Available edge vertical projection claimed to be robust to illumination, however, it tends to create false edges and sensitive to noises that can hinder the recognition performance. Thus, this research aims to propose a license plate localization method that based on exploitable edge analysis.comprising four main steps, namely pre-processing, rectangular blob searching, analysis and the vertical rectangular blobs projection. It calculates total and exploit edge information at y axis of the image. The proposed method is then tested on the European number plate datasets i.e. Baza Slika which contains about 167 vehicle images and Ondrej which contains about 97 vehicle images. The experimental results show that the proposed method outperforms the Ondrej method by obtaining accuracy of 95% on Baza Slika dataset and slightly lower by an accuracy of 91% on the Ondrej dataset. Then, the proposed method tested on the Malaysia vehicle dataset namely Tol Sungai Long data set which contains about 584 images of different illumination conditions, i.e. 297 images in the morning, 140 images in the afternoon and 147 images in the night. The proposed method outperforms other approaches with accuracy of 91.24%, 93.57% and 75.51% in the morning, evening and night respectively.

Dynamic License Plate Localization for Vehicles on Multi-lane Using Single Camera

2013 ◽  
Vol 300-301 ◽  
pp. 740-745
Author(s):  
Hung Li Tseng ◽  
Chao Nan Hung ◽  
Chiu Ching Tuan ◽  
You Ru Wen ◽  
Wen Tzeng Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Recognition System ◽  
License Plate ◽  
Single Camera ◽  
License Plate Recognition ◽  
Hardware Cost ◽  
Localization System ◽  
Parking Management ◽  
Toll Collection ◽  
Localization Systems

LPR (License Plate Recognition) System has been widely used in highway toll collection, parking management, various traffic regulations enforcement and other systems. Currently, most of the existing LPL (license plate localization) systems are with single camera that is limited to recognizing vehicles in one lane. In this paper we design a license plate localization system that simultaneously recognizes license plates of vehicles on multi-lane by using single high-resolution camera. Our approach significantly reduces the hardware cost of LPR system without sacrificing the accuracy of recognition. And our success rate is about 94%.

The Application of Image Processing Technology in the Intelligent Transportation System

2014 ◽  
Vol 543-547 ◽  
pp. 2678-2680 ◽  
Author(s):  
Xiu Hua Teng
Keyword(s):  
Image Processing ◽  
Intelligent Transportation System ◽  
Mean Shift ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Vehicle Recognition ◽  
Research Fields ◽  
Plate Location ◽  
Model Recognition

Image processing-based vehicle recognition is one of the important research fields in ITS. The existing methods are all based on license plate recognition and car shape recognition. Their common problem is algorithm stability. And the license plates are easy to be changed. All information about vehicles should be used to recognize them reliably. A problem to be solved is to find a method to recognize vehicles besides license plate recognition and vehicle model recognition. Vehicle license plate location and character segmentation are critical steps in the license plate recognition system, and yet there are difficult problems to be solved. Kernel density estimation and Mean Shift theory

Research on Pre-Processing Methods for License Plate Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 47-79
Author(s):  
Weifang Zhai ◽  
Terry Gao ◽  
Juan Feng
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Traffic Management ◽  
Recognition System ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Projection ◽  
License Plate Location ◽  
Plate Location

The license plate recognition technology is an important part of the construction of an intelligent traffic management system. This paper mainly researches the image preprocessing, license plate location, and character segmentation in the license plate recognition system. In the preprocessing part of the image, the edge detection method based on convolutional neural network (CNN) is used for edge detection. In the design of the license plate location, this paper proposes a location method based on a combination of mathematical morphology and statistical jump points. First, the license plate area is initially located using mathematical morphology-related operations and then the location of the license plate is accurately located using statistical jump points. Finally, the plate with tilt is corrected. In the process of character segmentation, the border and delimiter are first removed, then the character vertical projection method and the character boundary are used to segment the character for actually using cases.

Research on License Plate Recognition System Based on Computer Vision

2011 ◽  
Vol 65 ◽  
pp. 536-541
Author(s):  
Ye Qin Wang ◽  
Liang Hai Chen ◽  
Li Yun Zhuang
Keyword(s):  
Interface Design ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Binary Image ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Projection ◽  
Differential Operation ◽  
Program Transfer

In order to achieve the exact location and character recognition of license plate, firstly, this paper got binary image of license plate and done edge detection with differential operation. Secondly, it searched the license plate binary image after difference for the horizontal and vertical cut point, and determined the best cutting threshold through the experiment. Finally, it made the character segmentation by vertical projection, the recognition of license plate characters with the use of BP neural network, whose overall recognition rate is at 95.3%, and the display interface design for program transfer and results display. The experimental results showed that the location of license plate was exact and the character recognition rate was high.

scholarly journals WiGR: A Practical Wi-Fi-Based Gesture Recognition System with a Lightweight Few-Shot Network

2021 ◽  
Vol 11 (8) ◽  
pp. 3329
Author(s):  
Pengli Hu ◽  
Chengpei Tang ◽  
Kang Yin ◽  
Xie Zhang
Keyword(s):  
Computational Complexity ◽  
Gesture Recognition ◽  
Channel State Information ◽  
Domain Adaptation ◽  
Recognition Performance ◽  
Recognition System ◽  
Channel State ◽  
Data Set ◽  
State Information ◽  
Cross Domain

Wi-Fi sensing technology based on deep learning has contributed many breakthroughs in gesture recognition tasks. However, most methods concentrate on single domain recognition with high computational complexity while rarely investigating cross-domain recognition with lightweight performance, which cannot meet the requirements of high recognition performance and low computational complexity in an actual gesture recognition system. Inspired by the few-shot learning methods, we propose WiGR, a Wi-Fi-based gesture recognition system. The key structure of WiGR is a lightweight few-shot learning network that introduces some lightweight blocks to achieve lower computational complexity. Moreover, the network can learn a transferable similarity evaluation ability from the training set and apply the learned knowledge to the new domain to address domain shift problems. In addition, we made a channel state information (CSI)-Domain Adaptation (CSIDA) data set that includes channel state information (CSI) traces with various domain factors (i.e., environment, users, and locations) and conducted extensive experiments on two data sets (CSIDA and SignFi). The evaluation results show that WiGR can reach 87.8%–94.8% cross-domain accuracy, and the parameters and the calculations are reduced by more than 50%. Extensive experiments demonstrate that WiGR can achieve excellent recognition performance using only a few samples and is thus a lightweight and practical gesture recognition system compared with state-of-the-art methods.

Study of License Plate Recognition Technology

2013 ◽  
Vol 834-836 ◽  
pp. 1035-1038 ◽  
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Recognition System ◽  
Mean Value ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Projection ◽  
Traffic System ◽  
Vehicle Identification ◽  
Intelligent Traffic System ◽  
Mean Value Method ◽  
Test Result

License plate recognition technology has been widely used with the development of intelligent traffic system, which studies vehicle identification based on digital image processing technology. This paper presents system design and realization of recognition system for license plate. License plate image is preprocessed by gradation and binaryzation at first, then the image noise caused by dirt is filtered by a mean value method. We adopt horizontal and vertical projection method to locate license plate. Character segmentation and recognition are carried out at last. Test result shows that the method presented has good accuracy, quick speed and strong robustness for realtime application.

Automatic Number plate detection for law enforcement using optical recognition system

2021 ◽  
pp. 50-56
Author(s):  
Meenu Gupta ◽  
◽  
Rakesh Kumar ◽  
Arpit Sood ◽  
Gagandeep Kaur ◽  
...  
Keyword(s):  
Law Enforcement ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
License Plate ◽  
Lighting Condition ◽  
Location Data ◽  
Optical Character ◽  
Toll Collection ◽  
Infrared Cameras

Automatic Number Plate Recognition (ANPR) is a specialized type of Optical Character Recognition System (OCR). It is a method of reading a vehicle's license plate using OCR to create vehicle registry or location data. ANPR is utilized by a variety of agencies around the world to enforce the law, including determining whether a vehicle is registered or not. Government entities, such as highways agencies, can categorize traffic movements for computerized toll collection. Images of the text from the license plate can be stored and processed using the ANPR system. Infrared cameras are often employed to take photographs in any lighting condition, whether it is day or night. To be more accurate ANPR technology should also consider plate variations from place to place.

scholarly journals Indonesian license plate recognition with improved horizontal-vertical edge projection

2021 ◽  
Vol 21 (2) ◽  
pp. 811
Author(s):  
Ida Nurhaida ◽  
Imam Nududdin ◽  
Desi Ramayanti
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Specular Reflection ◽  
Region Of Interest ◽  
License Plate ◽  
Morphological Operations ◽  
License Plate Recognition ◽  
Vertical Edge ◽  
Vertical Projection ◽  
Real Scenario

<p>License plate recognition (LPR) is one of the classical problems in the field of object recognition. Its application is very crucial in the automation of transportation system since it helps to recognise a vehicle identity, which information is stored in the license plate. LPR usually consists of three major phases: pre-processing, license plate localisation, optical character recognition (OCR). Despite being classical, its implementation faced with much more complicated problems in the real scenario. This paper proposed an improved LPR algorithm based on modified horizontal-vertical edge Projection. The method uses for detecting and localising the region of interest. It is done using the horizontal and vertical projection of the image. Related works proved that the modified horizontal-vertical edge projection is the simplest method to be implemented, yet very effective against Indonesian license plate. However, its performance gets reduced when specular reflection occurs in the sample image. Therefore, morphological operations are utilised in the pre-processing phase to reduce such effects while preserving the needed information. Eighty sample images which captured using various camera configurations were used in this research. Based on the experimental results, our proposed algorithm shows an improvement compared with the previous study and successfully detect 71 license plates in 80 image samples which results in 88.75% accuracy.</p>

Automatic License Plate Recognition System

2010 ◽  
Vol 20-23 ◽  
pp. 438-444 ◽  
Author(s):  
Bo Li ◽  
Zhi Yuan Zeng ◽  
Hua Li Dong ◽  
Xiao Ming Zeng
Keyword(s):  
Character Recognition ◽  
Rbf Neural Network ◽  
Recognition System ◽  
Edge Density ◽  
Color Model ◽  
License Plate ◽  
License Plate Recognition ◽  
Color Information ◽  
Vertical Projection ◽  
Hsv Color Model

This paper proposed an algorithm for license plate recognition system(LPRS). The vertical edge was first detected by sobel color edge detector. Then, the invalid edge was removed regarding edge density. Next, the license plate(LP) image was converted into HSV color model, and by edge density template and fuzzy color information judgement, the LP region was located. Then, color-reversing judgement and tilt correction was conducted. Afterward, characters were segmented by means of vertical projection and convolution, by which character width and position can be exactly confirmed, and character recognition was conducted based on radial basis function (RBF) neural network. With a lot of samples verified in night hours and daytime under real conditions, the experimental results show that the proposed method can achieve accuracy and effectiveness in LPRS.

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