Morphology-based license plate detection from complex scenes

Hsieh, Jun-Wei; Yu, Shih-Hao; Chen, Yung‐Sheng

doi:10.1109/icpr.2002.1047823

Cited by 85 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional methods mainly extract features of objects according to the background color, edge and contour of the target. For example, Hsieh et al [9] utilized morphological methods to significantly reduce the number of candidate images extracted from cluttered images, which speeds up the subsequent license plate recognition. This method improves the effectiveness and robustness of license plate detection.…”

Section: A License Plate Detectionmentioning

confidence: 99%

A Robust License Plate Recognition Model Based on Bi-LSTM

et al. 2020

View full text Add to dashboard Cite

License plate detection and recognition are still important and challenging tasks in natural scenes. At present, most methods have favorable effect on license plate recognition under restrictive conditions, and most of such license plates are shot under good angle and light conditions. However, for license plates under non-restrictive conditions, such as dark, bright, rotated conditions etc. from the Chinese City Parking Dataset (CCPD), the performance of some methods of license plate recognition will be significantly reduced. In order to improve the accuracy of license plate recognition under unrestricted conditions, a robust license plate recognition model is proposed in this paper, which mainly includes license plate feature extraction, license plate character localization, and feature extraction of characters. First of all, the model can activate the regional features of characters and fully extract the character features of license plates. Then locate each license plate character through Bi-LSTM combined with the context location information of license plates. Finally, 1D-Attention is adopted to enhance useful character features after Bi-LSTM positioning, and reduce useless character features to realize effective acquisition of character features of license plates. A large number of experimental results demonstrate that the proposed algorithm has good performance under unrestricted conditions, which proves the effectiveness and robustness of the model. In CCPD-Base, CCPD-DB, CCPD-FN, CCPD-Tilt, CCPD-Weather, CCPD-Challenge and other sub-datasets, the recognition rates reach 99.3%, 98.5%, 98.6%, 96.4%, 99.3% and 86.6% respectively.

show abstract

Section: A License Plate Detectionmentioning

confidence: 99%

A Robust License Plate Recognition Model Based on Bi-LSTM

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Opening is the opposite of the closing process. The method of opening the vehicle detection aims to soften the edges of the object [16]. The equation for the opening technique can be seen in the following equation (4).…”

Section: Morphological Operationmentioning

confidence: 99%

Vehicle Detection and Counting to Identify Traffic Density in The Intersection of Road Using Image Processing

Lahinta¹,

Zainuddin²,

Syarif³

2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

View full text Add to dashboard Cite

Vehicle density information for traffic regulation including the timing of traffic lights is still very minimal. This study aims to calculate the number of vehicles at an intersection then classify the density level of each road segment. The detection process begins with taking video from Teling intersection of Manado City, Indonesia. Video processed using the Gaussian Mixture Model (GMM) algorithm and Morphological Operation (MO) to detect vehicles object in the form of BLOB (Binary Large Object). The results of the feature extraction are calculated to get the number of vehicles from the specified Region of Interest (ROI). The results of counting vehicles are classified according to the density level to be able to compare the level of congestion on each road segment. The results of the proposed system accuracy is 90.9% for the calculation of vehicles on the road. This research is expected to be implemented in Smart Traffic Light.

show abstract

“…Morphology based method for license plate detection is proposed in [16]. The authors apply a morphology based method for detecting important contrast features.…”

Section: Related Workmentioning

confidence: 99%

Vehicle License Plate Detection and Perspective Rectification

Alhussein¹,

Aurangzeb²,

Haider³

2019

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

The character segmentation and perspective rectification of Vehicle License Plate (VLP) is essential in different applications, including traffic monitoring, car parking, stolen vehicle recovery, and toll payment. The character segmentation of the VLP and its horizontal as well as vertical (pan and tilt) correction is a crucial operation. It has considerable impact on the precision of the vehicle identification process. In this work, we investigate an effective framework for the perspective rectification and homography correction of vehicle's images. The captured images of the vehicle could be tilted in vertical or horizontal or vertical-horizontal mix directions due to different movements. For reasonable high identification results, a polynomial fitting based homography correction method for rectifying the tilted VLPs is applied. A method for determining four corner points of the rotated VPLs is explored. These four detected corner points are applied in the homography correction algorithm. For comprehensively evaluating the performance of the proposed framework, the detected VLPs in various directions, such as horizontal, vertical, and mix horizontal-vertical, are rotated. For the experiments, the real images of the vehicles in the outdoor environment, from different directions and different distances are captured. With our proposed method, we achieve an accuracy of 97 % and 95 % for the simulated and real captured images, respectively.

show abstract

Morphology-based license plate detection from complex scenes

Cited by 85 publications

References 6 publications

A Robust License Plate Recognition Model Based on Bi-LSTM

A Robust License Plate Recognition Model Based on Bi-LSTM

Vehicle Detection and Counting to Identify Traffic Density in The Intersection of Road Using Image Processing

Vehicle License Plate Detection and Perspective Rectification

Contact Info

Product

Resources

About