Multiple License Plate Detection for Complex Background

[[abstract]]This paper presents a wavelet transfonn based method for extracting license plates from cluttered images. The proposed system consists of three major stages. First, a wavelet transfonn based method is used for extracting important contrast features as guides to search for desired license plates. Then, finding a reference line in HL subimage plays an important role to locate the desired license plate region roughly. According to the reference line we can decrease the searching region of license plate and speed up the execution time. The last stage of the method is to locate the license plate accurately by license plate adjustment. More importantly, the proposed detection method can locate multiple plates with different orientations in one image. Since the feature extracted is robust to complex backgrounds, the proposed method works well in extracting differently illuminated and oriented license plates. The average accuracy of detection is 92.4%.[[sponsorship]]IEEE Computer Society Technical Committee on Distributed Processing (TCDP); Tamkung University[[conferencetype]]國際[[conferencetkucampus]]淡水校園[[conferencedate]]20050328~20050330[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]臺北縣, 臺

Learning-based license plate detection using global and local features

This paper proposes a license plate detection algorithm using both global statistical features and local Haar-like features. Classifiers using global statistical features are constructed firstly through simple learning procedures. Using these classifiers, more than 70% of background area can be excluded from further training or detecting. Then the AdaBoost learning algorithm is used to build up the other classifiers based on selected local Haar-like features. Combining the classifiers using the global features and the local features, we obtain a cascade classifier. The classifiers based on global features decrease the complexity of the system. They are followed by the classifiers based on local Haar-like features, which makes the final classifier invariant to the brightness, color, size and position of license plates. The encouraging detection rate is achieved in the experiments. © 2006 IEEE

Recognition of vehicle registration mark on moving vehicles in an outdoor environment

In this paper, we propose a new method for segmenting vehicle registration plate and recognizing the registration mark on a moving vehicle in an outdoor environment. The algorithm first segments the plate from the vehicle and other complex objects in view, based on the plate color and its dimension. The segmented plate is then corrected in orientation and size, before being matched with a sub-set of templates in the database. The matching adopts a high tolerant scheme allowing characters to have certain degree of shifting, rotation and mis-match. This proves to be an important criterion for successful recognition. Preliminary tests show that this method offers high success rate and high confident level.published_or_final_versio

A fast algorithm for license plate detection in various conditions

This paper proposes a fast algorithm detecting license plates in various conditions. There are three main contributions in this paper. The first contribution is that we define a new vertical edge map, with which the license plate detection algorithm is extremely fast. The second contribution is that we construct a cascade classifier which is composed of two kinds of classifiers. The classifiers based on statistical features decrease the complexity of the system. They are followed by the classifiers based on Haar-features, which make it possible to detect license plate in various conditions. Our algorithm is robust to the variance of the illumination, view angle, the position, size and color of the license plates when working in complex environment. The third contribution is that we experimentally analyze the relations of the scaling factor with detection rate and processing time. On the basis of the analysis, we select the optimal scaling factor in our algorithm. In the experiments, both high detection rate (with low false positive rate) and high speed are achieved when the algorithm is used to detect license plates in various complex conditions. © 2006 IEEE

Car license plate detection method for Malaysian plates-styles by using a web camera

Recently, license plate detection has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them work under restricted conditions such as fixed illumination, stationary background, and high resolution images. License plate detection plays an important role in car license plate recognition systems because it affects the accuracy and processing time of the system. This work aims to build a Car License Plate Detection (CLPD) system at a lower cost of its hardware devices and with less complexity of algorithms' design, and then compare its performance with the local CAR Plate Extraction Technology (CARPET). As Malaysian plates have special design and they differ from other international plates, this work tries to compare two likely-design methods. The images are taken using a web camera for both the systems. One of the most important contributions in this paper is that the proposed CLPD method uses Vertical Edge Detection Algorithm (VEDA) to extract the vertical edges of plates. The proposed CLPD method can work to detect the region of car license plates. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 out of 643 images were successfully detected. Meanwhile, the average accuracy of locating car license plate was 90%. In this work, a comparison between CARPET and the proposed CLPD method for the same tested images was done in terms of detection rate and efficiency. The results indicated that the detection rate was 92% and 84% for the CLPD method and CARPET, respectively. The results also showed that the CLPD method could work using dark images to detect license plates, whereas CARPET had failed to do so

Modeling and Implementing Two-Stage AdaBoost for Real-Time Vehicle License Plate Detection

License plate (LP) detection is the most imperative part of the automatic LP recognition system. In previous years, different methods, techniques, and algorithms have been developed for LP detection (LPD) systems. This paper proposes to automatical detection of car LPs via image processing techniques based on classifier or machine learning algorithms. In this paper, we propose a real-time and robust method for LPD systems using the two-stage adaptive boosting (AdaBoost) algorithm combined with different image preprocessing techniques. Haar-like features are used to compute and select features from LP images. The AdaBoost algorithm is used to classify parts of an image within a search window by a trained strong classifier as either LP or non-LP. Adaptive thresholding is used for the image preprocessing method applied to those images that are of insufficient quality for LPD. This method is of a faster speed and higher accuracy than most of the existing methods used in LPD. Experimental results demonstrate that the average LPD rate is 98.38% and the computational time is approximately 49 ms

Vehicle license plate detection and recognition

"December 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Zhihai He.In this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process. Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We tested our method on 200 real life images, captured on Chinese highways under different weather conditions and lighting conditions. And we achieved a detection rate of 100%. After detecting license plates, alignment is then performed on the plate candidates. Conceptually, this alignment method searches neighbors of the bounding box detected, and finds the optimum edge position where the outside regions are very different from the inside regions of the license plate, from color's perspective in RGB space. This method accurately aligns the bounding box to the edges of the plate so that the subsequent license plate segmentation and recognition can be performed accurately and reliably. The system performs license plate segmentation using global alignment on the binary license plate. A global model depending on the layout of license plates is proposed to segment the plates. This model searches for the optimum position where the characters are all segmented but not chopped into pieces. At last, the characters are recognized by another SVM classifier, with a feature size of 576, including raw features, vertical and horizontal scanning features. Our character recognition results show that 99% of the digits are successfully recognized, while the letters achieve an recognition rate of 95%. The license plate recognition system was then incorporated into an embedded system for parallel computing. Several TS7250 and an auxiliary board are used to simulIncludes bibliographical references (pages 67-73)

Vision-based Detection of Mobile Device Use While Driving

The aim of this study was to explore the feasibility of an automatic vision-based solution to detect drivers using mobile devices while operating their vehicles. The proposed system comprises of modules for vehicle license plate localisation, driver’s face detection and mobile phone interaction. The system were then implemented and systematically evaluated using suitable image datasets. The strengths and weaknesses of individual modules were analysed and further recommendations made to improve the overall system’s performance