Shadow aware license plate recognition system

El-Said, Shaimaa Ahmed

doi:10.1007/s00500-014-1245-5

Cited by 12 publications

(9 citation statements)

References 11 publications

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“…Based on the theoretical concepts of the study of El-Said [26], to use the identification of license plate localization and detection for daytime objects is executed for different and respective traffic lanes for extracting accurately features of digital video image from transportation tools. Figure 4 shows an example of schematic diagram in the identification of daytime objects for a real environment in Taiwan.…”

Section: Methodsmentioning

confidence: 99%

“…Correspondingly, it has some difficult for extracting adequately the identification of nighttime image from moving objects due to its insufficient light sources when comparing to daytime condition; thus, it needs more enough technique processing to address this problem, such as the study of Kannan [11,26,27]. Figure 5 shows a case of schematic diagram in identifying accurately the nighttime objects.…”

Section: Methodsmentioning

confidence: 99%

“…From literature review of the study in Zheng et al [27], an effective method of using infrared detector and light source to detect moving points on objects is employed with both purposes of the verification and examination from special pattern images for extracting the characteristics on the special case of vehicle, such as a business taxi. Figure 6 lists a schematic diagram for identifying the special case of vehicle by a technique of infrared detector [26].…”

Section: Methodsmentioning

confidence: 99%

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An Advanced ICTVSS Model for Real-Time Vehicle Traffic Applications

Chen

Lin

Kan

2019

Sensors

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From the accident news, it is found that the occurrences of traffic accidents every year and the numbers of deaths and injuries have raised continually and have become a specific issue concerned in society in Taiwan. More seriously, the number of traffic accidents is positively increased with the increasing motorized vehicles. Thus, to reduce the incidence of traffic accidents through by some advanced real-time technologies is an important and interesting work. However, some serious problems against traffic safety are facing, such as the low-quality video saved by a camera, low efficiency facilities supported, inefficient management of surveillance resources, and low definition resolution for cameras, which is resulted in a dilemma problem caused from providing evidence-based images to a local authority either for criteria for judgment or basis for improvement. As a big effort to deal with the above defects for constructing a smart city, this paper makes a main purpose to develop an advanced system of intelligent cloud-based transportation vehicle surveillance (called ICTVSS) for license plate identification. This existing identification algorithm was studied and developed from a combination of improved differential algorithm and improved active contour algorithm. Given such a combination, a novel algorithm of dynamic license identification for smart monitoring was fully realized for constructing a well-defined smart city. The experimental results showed good performance and experienced that the proposed algorithm performed well in locating multi-license plate and differential methods, removing image noise of license plate, and processing constant-inconstant light source from complex environment cases, and guaranteed effective license plate identification from the benefit of high resolutions of digital cameras.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An Advanced ICTVSS Model for Real-Time Vehicle Traffic Applications

Chen

Lin

Kan

2019

Sensors

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“…1. Cast shadow parts : "umbra" and "penumbra" [2] A spot light source only causes the appearance of a dark spot in the shadow. An area of light source, however, causes the appearance of both a dark spot and penumbra.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Method for Detecting the Shaded Images of the Car License Plates based on Histogram Equalization and Probabilities

Faghedi¹,

Barekatain²,

Raahemifar³

2018

ijacsa

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Shadow is one of the major and significant challenges in detection algorithms which track the objects such as the license plates. The quality of images captured by cameras is influenced by weather conditions, low ambient light and low resolution of the camera. The shadow in images reduces the reliability of the sight algorithms of the device as well as the visual quality of images. The previous papers indicate that no effective method has been presented to improve the license plate detection accuracy of the shaded images. In other words, the methods that have been presented for automatic license plate detection in shadowed images until now use a combination of color features and texture of the image. In all these methods, in order to detect the frame of the shadow and the texture of the image, sufficient light is required in the image; this necessity cannot be found in most of the regular images captured by road cameras. In order to solve this problem, an improved license plate detection method is presented in this research which is able to detect the license plate area in shadowed images effectively. In fact, this is a contrast-improving method which utilizes the dual binary method for automatic plate detection and is introduced to analyze the interior images with low contrast, and also night shots, blurred and shadowed images. In this method, the histogram of the image is firstly calculated for each dimension and then the probability of each pixel in the whole image is obtained. As a result, after calculating the cumulative distribution of the pixels and replacing it in the image, it will be possible to remove the shadow from the image easily. This new method of detection was tested and simulated for 1000 images of vehicles under different conditions. The results indicated the detection accuracy of 90/30, 97/87 and 98/70 percent for the license plates detection in three databases of University of Zagreb, Numberplates.com and National Technical University of Athens, respectively. In other words, comparing the performance of the proposed method with two similar and new methods, namely Hommos and Azam, indicates an average improvement of 26/70 and 72/95 percent for the plate detection and 32/38 and 36/53 percent for the time required for rapid and correct license plate detection, even in shaded images.

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“…This greatly affects geometrical properties and appearance of the object to be detected [3]. Shadow can cause inaccuracy in some applications, such as the applications for plant leaves segmentation [4], license plate recognition [5], gait recognition [6,7], analysis of remote-sensing images [8,9], classification of objects from video surveillance system [10], underwater object detection [11] and clustering [12].…”

Section: Introductionmentioning

confidence: 99%

Development of Algorithm to Reduce Shadow on Digital Image

Ooi¹,

Ibrahim²

2016

IJIGSP

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Abstract-In this paper, two shadow reduction algorithms have been proposed and implemented using CIE Lab color space. The task of performing shadow reduction is done by executing shadow detection, shadow removal and lastly shadow edge correction in a sequential order. The first proposed algorithm is implemented based on pixel illumination and color information meanwhile the second algorithm is carried out via thresholding of one or more CIE Lab color space channels. The outputs from both proposed algorithms are compared in terms of shadow detection accuracy and required processing period. The proposed methods shown some promising results.

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Shadow aware license plate recognition system

Cited by 12 publications

References 11 publications

An Advanced ICTVSS Model for Real-Time Vehicle Traffic Applications

An Advanced ICTVSS Model for Real-Time Vehicle Traffic Applications

An Enhanced Method for Detecting the Shaded Images of the Car License Plates based on Histogram Equalization and Probabilities

Development of Algorithm to Reduce Shadow on Digital Image

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