At present, There is the main algorithm is based on the morphological features for the license plate location problem. The algorithm is mainly using the texture characteristics of car license plate. After processing on the morphology, It takes exclusion of a part of the interference of the noise in the background of license plate images, and then based on the geometric features of license plate, such as aspect ratio condition to locate the license plate location. Due to the use of the algorithm is only a single plate texture feature, ignoring the license plate of the other characteristics of the information. So when the image background and license plate area is in the same objects with the texture feature, just rely on geometric features such as aspect ratio decision condition is difficult to determine whether to license plate area. Eventually led to the wrong location; The positioning algorithm based on color image [5]. This algorithm makes full use of the license plate in the image color information, through the characteristics of the different color space to locate the license plate. But the color model is operate in the image on the multi-channel models. It has large amount of calculation and poor real-time performance. When the license plate region color is very similar to surroundings, the algorithm positioning error rate increase. And the color of the license plate image information is susceptible to interference illumination change, which made an impact on the extraction of license plate character. Eventually lead to the wrong location or position; The algorithm based on BP neural network. The algorithm is divided the image grid precessed into many blocks, using neural network for each block region extracted feature descriptor to classify and locate the license plate. But the extraction of license plate image characteristics of image block area size has nothing to do with the actual license plate image size. The global characteristics can not truly reflect the global features of the license plate. Convergence time is long, and BP algorithm easy to fall into local optimum.
Analysis of previous license plate localization algorithm based on texture feature. After the license plate image by the morphological processing can effectively make the license plate area rectangle connected area. But it is difficult to further improve the accuracy of this method. If the interference area of the image and texture features of license plate is similar, then interference region to form a rectangular connected domain also. So just rely on license plate aspect ratio to determine conditions to distinguish the license plate region and the license plate region is difficult. This makes the poor anti-jamming, misjudgment rate is high. Aimed at the problem in rectangular area filter link using the visual word packet and support vector machine (SVM) to improve filtering accuracy. Basic principle of visual word package features is to use characteristic descriptors to express images, and put the picture as a different set of feature points. Through the statistics of each feature point frequency in the single photo to vectors to a photograph. Namely in the form of a histogram to represent the photograph [7]. As shown in figure 1 package basic process for visual words. Due to the different types of pictures’ vector representation from it’s visual word package are different, so it can choose the appropriate classifier using sample set for training. Then use the trained classifier to classify test images.
Flow chart of BOW model
Considering the license plate images will get affected by Angle, light and other factors. In order to reduce such factors on the characteristics of the descriptor to describe the influence of the license plate area, and can achieve rapid positioning of the target. This paper adopted the surf feature descriptor. The descriptor is characterized by has fast calculation and partial invariance. That is to say, it has the scale of the robustness of a certain range scaling, image rotation, the change of perspective, illumination changes and image blur, can effectively eliminate from light, Angle and other factors. Extraction algorithm of surf feature descriptor is Hessian matrix is used to determine the candidate points. Not greatly restrain and realize the feature point detection. In the image pixel Hessian matrix are defined as follows:
Hessian matrix discriminant such as type (2). The value of the discriminant is the eigenvalues of the Hessian matrix. It classifies all points using symbols of determining structure and according to the discriminant value plus or minus identifying whether this point is the value of the pole.
This algorithm use image pixel L (x, y) to replace function f (x, y) and use type (1) through specific nuclear convolution between to computation the second order partial derivative, it is concluded that the Hessian matrix of the matrix elements is
Feature points
In order to guarantee the rotation invariance, we need to calculate Centered on feature points and the sum of the corresponding Harr wavelet for 60 degrees within the sector is all points in the direction of the x and y. Close to feature points of the weight is big, away from is small. To get the main direction of each feature point. This process is shown in figure 3.
Determine the main direction
Although can Surf characteristics describe a image, an image contains a large number of Surf feature points. If the training directly used in classification, calculation will be very big. We need through the clustering algorithm to cluster the vector data. Using a cluster of clusters represent visual words in a visual word, and then map the surf feature points to the visual word package generated code. In this paper, using K means clustering algorithm in constructing the visual word package. The principle of algorithm is simple and easy to implement and there is a good clustering effect. Such as type (3) the calculation formula. E is sum of square error for all clustering objects. P is the clustering objects. |
The problem of License plate location only need to classify an outline of the license plate and the plate profile picture. We chose SVM, which is the classifier of dichotomy. SVM is a kind of machine learning methods based on the theory of VC dimension and the theory of structural risk minimization. It is outstanding in solving nonlinear problems and it was originally designed for binary classification problems [8]. Principle of SVM is put data map to high-dimensional space, then finding largest classification interval hyperplane in high dimensional space, and using the hyperplane to classify. Such as type (4) calculation formula. (
Image contains abundant information. The object shape, size, color, texture, and other characteristics. The image is defined as the graph of graph theory, are defined as follows:
Among them,
Among them,
For no edge connection between segmentation part, is S. For the segmentation boundary judgment are defined as follows:
The smallest division of internal difference are defined as follows:
Among them,
Image segmentation
In the image object is hierarchical. After initial segmented regions, using similarity to calculate diversification and region merging. For area merger adopt four similarity in the image as follows:
To combine the calculated four kinds of similarity. Similar set is
The
The suspicious area extraction
9*9 filtering templateFigure 6.
The filter size of Octave
Generate surf feature points
Surf feature points
Surf feature points are extracted. Using the k - means clustering algorithm to generate visual word package. [10] All surf feature points’ samples extracted in the training library are {
Then select 1000 cluster centroid point are
To calculate each sample i should belong to the class that is
The training of the classifier. Using visual word package to express the picture of the training set. That is to say, extracting surf feature points of images and mapping it to the corresponding word package. To generate the code of the picture. Inputting into SVM to take training, the process is as follows:
Gaving each sample
To select the gaussian kernel function as a conversion function that cast onto the n dimensional vector. Selecting its σ value control the dimension of the projection. Selection of penalty factor
All the sample are substituted into type (4) and to calculation, get the classification of the decision function in the form below:
In the recognition phase, through visual word package said the image into vector form. Using the trained SVM classifier to classify its license plate images. Locating the license plate location.
Aimed at the phenomenon of laminated object in the image, using based on graph search algorithm to obtain the suspicious area of vehicle license plate in the image. Extracting its feature points of surf for coarse positioning of the rectangular profile area. According to generated bag of Visterms represented the candidate images as codebook. Using decisions classification function are obtained by training SVM to classify rectangular area, locating the license plate. This method have higher recognition rate and anti-jamming is strong under complex background. The collection of 140 photos of the result: its accuracy is 135 pieces, accuracy is 96.4%, has the strong robustness.