Extraction and recognition of license plates of motorcycles and vehicles on highways

Lee, Hsi-Jian; Chen, Siyuan; Wang, Shen-Zheng

doi:10.1109/icpr.2004.1333776

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…There are very limited works on the detection of motorcycle license plates, and [1,2] are a couple of the rare. The pixel intensity projection in [1], like many conventional intensity projection approaches [3], can only handle upright plates, and fail to handle rotated plates, cluttered backgrounds and uneven illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The pixel intensity projection in [1], like many conventional intensity projection approaches [3], can only handle upright plates, and fail to handle rotated plates, cluttered backgrounds and uneven illumination conditions. The method in [2] exploits a 2-stage approach by detecting the motorcycle first and then locating the plate using fixed-size scanning window and multiple mean analysis.…”

Section: Introductionmentioning

confidence: 99%

“…We have expanded our survey to the automobile license plate detection with potential to be applicable on motorcycles. The extensive survey given in [3] splits a wide range of methods into four categories: binary processing, gray-level processing [4,1,5], color processing [6] and classification [7,8,9]. The first three are more or less heuristic or ad-hoc approaches but the last one is learning based in nature.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparison study on motorcycle license plate detection

Hsu

Zeng

Chiu

et al. 2015

2015 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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License plate detection and recognition are mostly studied on automobiles but only few on motorcycles. As motorcycles are becoming popular for local transportation and environmental friendliness, the demands for license plate recognition have been increasing in recent years. The primary difference between the license plate recognition in automobiles and motorcycles is on the detection of license plates, which is the topic of this study. For automobiles, the license plates are mostly installed on the front or on the back of the vehicle with relatively less complicated backgrounds; however, for motorcycles, the backgrounds can be far more complicated. To better handle complicated backgrounds, we study the case with motorcycle detection as preprocessing so that the search area for the license plate can be better constrained, and compare its performance with the case without the preprocessing. A few detection methods are configured and studied for both the motorcycle detection and license plate detection, including the state-of-the-art part-based model. Considering processing speed and accuracy, the histogram of oriented gradients (HOG) with support vector machines (SVMs) is found to be the best detector for motorcycle license plates.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparison study on motorcycle license plate detection

Hsu

Zeng

Chiu

et al. 2015

2015 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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show abstract

“…To detect the region of car license plate, many techniques have been used. In [8] combination of edge statistics and mathematical morphology showed very good results, but it is time consuming and because of this problem, [9] uses block-base algorithm. In [10] a novel method called "N row distance" is implemented.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Multiple License Plate Recognition System

Azad

Ahmadzade²

2014

International Journal of Research in Computer Science

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License plate recognition system is one of the core technologies in intelligent traffic control. In this paper, a new and tunable algorithm which can detect multiple license plates in high resolution applications is proposed. The algorithm to the investigation and identification of the novel Iranian and some European countries plate, characterized by both inclusion of blue area on it and its geometric shape. Obviously, the suggested algorithm contains suitable velocity due to not making use of heavy preprocessing operation such as image-improving filters, edge-detection operation and omission of noise at the beginning stages. So the method recommended here is compatible with model-adaptation, i.e. using the very blue section of the plate. The present method indicates the fact that, if several plates are included in the image, this method can successfully manage to detect them. We evaluated our method on the two Persian single vehicle license plate data set that we obtained 99.33, 99% correct recognition rate respectively. Further we tested our algorithm on the Persian multiple vehicle license plate data set and we achieved 98% accuracy rate. Also we obtained approximately 99% accuracy in character recognition stage.

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“…There has also been some instant VLPR installed on the road [11][12]. The above mentioned applications of VLPR are still confined to gate control management.…”

Section: Introductionmentioning

confidence: 99%

A real-time mobile vehicle license plate detection and recognition for vehicle monitoring and management

Hsieh

Chang

Hung

et al. 2009

2009 Joint Conferences on Pervasive Computing (JCPC)

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In this paper we present a instant and real-time mobile vehicle license plate recognition system in an open environment. Using a nonfixed video camera installed in the car, the system tries to capture the image of the car in front and to process instant vehicle license plate detection and recognition. Relying on the instant vehicle body recognition, the system can detect and locate the vehicle license plate without the need ofbackground image. Vehicle body detection system utilizes the color characteristics ofthe barking lights to carry out detection. It first detects the location of the two barking lights in the captured image. Then set license plate detection region using the probability distribution of the license plate between the two lights, thus quickly locate the license plate. This method can eliminate any environmental interference during the license plate detection. From the results of experiment, it is determined that this system can effectively and quickly capture the vehicle image, detect and recognize the license plate whether it is dark, raining or under complicated environments.

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Extraction and recognition of license plates of motorcycles and vehicles on highways

Cited by 20 publications

References 9 publications

A comparison study on motorcycle license plate detection

A comparison study on motorcycle license plate detection

Real-Time Multiple License Plate Recognition System

A real-time mobile vehicle license plate detection and recognition for vehicle monitoring and management

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