Multiple licence plate detection for Chinese vehicles in dense traffic scenarios

Asif, Muhammad Rizwan; Qi, Chun; Hussain, Sajid; Fareed, Muhammad Sadiq

doi:10.1049/iet-its.2016.0008

Cited by 37 publications

(20 citation statements)

References 21 publications

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“…YDbDr has three components— Y , Db , and Dr. Y is the luminance, Db (blue color difference), and Dr (red color difference) are the chrominance components. Conversion between YDbDr and RGB color spaces is presented in Equation ,

([], \begin{array}{l} Y \\ italicDb \\ italicDr \end{array}) = ([], \begin{array}{l} 0.299 & 0.587 & 0.114 \\ - 0.450 & - 0.883 & 1.133 \\ - 1.333 & 1.116 & 0.217 \end{array}) ([], \begin{array}{l} R \\ G \\ B \end{array}) .

…”

Section: Color Spaces' Reviewmentioning

confidence: 99%

Review and evaluation of color spaces for image/video compression

Kahu

Raut²,

Bhurchandi

2018

Color Research & Application

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A color space plays an important role in color image processing and color vision applications. While compressing images/videos, properties of the human visual system are used to remove image details unperceivable by the human eye, appropriately called psychovisual redundancies. This is where the effect of the color spaces' properties on compression efficiency is introduced. In this work, we study the suitability of various color spaces for compression of images and videos. This review work is undertaken in two stages. Initially, a comprehensive review of the published color spaces is done. These color spaces are classified and their advantages, limitations, and applications are also highlighted. Next, the color spaces are quantitatively analyzed and benchmarked in the perspective of image and video compression algorithms, to identify and evaluate crucial color space parameters for image and video compression algorithms.

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([], \begin{array}{l} Y \\ italicDb \\ italicDr \end{array}) = ([], \begin{array}{l} 0.299 & 0.587 & 0.114 \\ - 0.450 & - 0.883 & 1.133 \\ - 1.333 & 1.116 & 0.217 \end{array}) ([], \begin{array}{l} R \\ G \\ B \end{array}) .

…”

Section: Color Spaces' Reviewmentioning

confidence: 99%

Review and evaluation of color spaces for image/video compression

Kahu

Raut²,

Bhurchandi

2018

Color Research & Application

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“…The proposed method was robust to illumination changes and weather conditions during 24 h or 1 day. Recently, Asif et al presented a simple yet effective colour detection method for CLPR, which performs under various weather and illumination conditions [4]. Experimental results showed that the accuracy of this method was 93.86% and the average processing time per image was 0.33 s. Wang et al used a back‐propagation neural network for CLPR.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, an increasing awareness of security has made the need for vehicle‐based authentication technologies very strong, with the idea that ALPR systems may be used as access control systems for monitoring of unauthorised vehicles that intrude into private areas [3]. In China, the license plates (LPs) of vehicles are rectangular and feature seven characters [4]. The first character on any LP in China must be a character in Chinese that represents the province of China with which the car is affiliated.…”

Section: Introductionmentioning

confidence: 99%

Chinese vehicle license plate recognition using kernel‐based extreme learning machine with deep convolutional features

Duan

2017

IET Intelligent Transport Systems

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“…It provides a reference as well for further vehicle activity analysis and tracking. VLPR system has become a core methodology because of its wide range of traffic applications along with security ranging from parking automation to vehicle surveillance, electrical tollgate management, restricted area security control, road traffic monitoring, analysis of vehicle activity, tracking for safety and calculating the traffic volume [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…VLPR system is generally composed of basic three stages which are the license plate detection (LPD), the character segmentation and the character recognition whilst LPD has emerged as the most important stage in the VLPR system since the scheme's accuracy gets influenced by it [3]. Moreover, because of the parameter diversities involved in the vehicle images, LPD has become the most crucial stage among these three steps [1,4].…”

Section: Introductionmentioning

confidence: 99%

A Vehicular License Plate Recognition Framework For Skewed Images

2018

TIIS

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Vehicular license plate (LP) recognition system has risen as a significant field of research recently because various explorations are currently being conducted by the researchers to cope with the challenges of LPs which include different illumination and angular situations. This research focused on restricted conditions such as using image of only one vehicle, stationary background, no angular adjustment of the skewed images. A real time vehicular LP recognition scheme is proposed for the skewed images for detection, segmentation and recognition of LP. In this research, a polar co-ordinate transformation procedure is implemented to adjust the skewed vehicular images. Besides that, window scanning procedure is utilized for the candidate localization that is based on the texture characteristics of the image. Then, connected component analysis (CCA) is implemented to the binary image for character segmentation where the pixels get connected in an eight-point neighbourhood process. Finally, optical character recognition is implemented for the recognition of the characters. For measuring the performance of this experiment, 300 skewed images of different illumination conditions with various tilt angles have been tested. The results show that proposed method able to achieve accuracy of 96.3% in localizing, 95.4% in segmenting and 94.2% in recognizing the LPs with an average localization time of 0.52s.

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Multiple licence plate detection for Chinese vehicles in dense traffic scenarios

Cited by 37 publications

References 21 publications

Review and evaluation of color spaces for image/video compression

Review and evaluation of color spaces for image/video compression

Chinese vehicle license plate recognition using kernel‐based extreme learning machine with deep convolutional features

A Vehicular License Plate Recognition Framework For Skewed Images

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