VEHICLE IDENTIFICATION AND AUTHENTICATION SYSTEM

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1 VEHICLE IDENTIFICATION AND AUTHENTICATION SYSTEM T.Anusha 1, T.Sivakumar 2 1 Assistant Professor, Dept. of Computer Science & Engineering, PSG College of Technology, Tamilnadu, India, anu@cse.psgtech.ac.in 2 Assistant Professor(Sr.Gr), Dept. of Information Technology, PSG College of Technology,Tamilnadu, India, sk@ity.psgtech.ac.in Abstract Vehicle Identification and Authentication System is developed for traffic monitoring. To prevent unauthorized vehicles from entering the private areas, vehicle based authentication technologies are employed. The captured color image of the vehicle is converted to gray scale image. Gray scale image is converted into binary image using Sliding Concentric Windows (SCW) method. Pixels are labelled into components based on pixel connectivity using Connected Component Analysis (CCA) technique. The labelled components are examined and detected license plate is processed to isolate characters. These characters are sent to Probabilistic Neural Network (PNN).PNN uses a supervised training set to develop distributed functions within a pattern layer. Vehicle entering an area is considered as authenticated if it is registered. Registered vehicles number plate indicates the state and district to which particular vehicle belongs to. Vehicles are considered to be authenticated, if it belongs to a particular state and district, after which the vehicles type and other details are extracted from the database. Index Terms: Image Segmentation, Interpolation, Sliding Concentric Window, Probabilistic Neural Network *** INTRODUCTION Vehicle Identification and Authentication system is developed to identify a vehicle and examine the permit details of it. Each vehicle possesses a unique license plate number. A still Red, Green, Blue (RGB) image of a vehicle is given as input. It is then converted into a gray-scale image. License plate is segmented from the image using Sliding Concentric Windows (SCW) method. In this algorithm, two concentric windows were created for the first pixel of the image. Mean of the two windows were calculated. If the ratio exceeds a threshold, then the central pixel of the windows is considered to belong to a Region of Interest (ROI). Threshold is calculated using Otsu s method, mean and standard deviation. Using Connected Component Analysis(CCA) method, various components are labeled. Properties such as area, orientation, and aspect ratios are examined and the license plate is detected. The detected license plate is small in size when compared with the original image and so the image is then resized to 75x228 pixels image using bicubic interpolation method. With this method, the value f(x,y) of a function f at a point (x,y) is computed as a weighted average of the nearest sixteen samples in a rectangular grid(4x4 array). The License plate image is further binarized using SCW method. The segmented characters are then recognized using PNN. PNN is a kind of radial basis network suitable for classification problems. It uses supervised training set clustered into classes for recognizing characters. Each vehicle is registered to a Regional Transport Office (RTO). In old registration series, first three characters identify the RTO district and state of the vehicle belonging to. In new registration series, first two characters identify state, second two characters identify the RTO district of the vehicle 2. REVIEW OF OTHER TECHNIQUES 2.1 License Plate Detection License plate region can be extracted by combining edge statistics and mathematical morphology. In this method, regions with a high edge magnitude and high edge variance are considered as possible license plate regions [5]. The four steps used for license plate location are vertical edge detection, edge statistical analysis, hierarchical based license plate location, and morphology based license plate extraction. Gray scale image f of the input image is smoothed using linear filter and normalized. Then horizontal edge map G H and vertical edge map G V are calculated using equations (1) and (2) G H (i,j)= [f(i-1,j-1)+2(f(i-1,j+1)]-[f(i+1,j- 1)+2(f(i+1,j)+f(i+1,j+1)] (1) Available 222

2 G v (i,j)= [f(i-1,j-1)+2(f(i,j-1)+f(i+1,j-1)]-[f(i- 1,j+1)+2f(i,j+1)+f(i+1,j+1)] (2) Points are combined to lines and lines are combined to rectangles. Rectangles are combined or deleted. The license plate is located through scaling using different thresholds. When low scale is chosen, some fake plates also will get detected. Plate regions tend to have high density of edges. Edge density is calculated by summing all edge pixels using equation (3) in 3x15 block, which is at center G(i,j): d(i,j)=(1/45) g v (i+x,j+y)mask(i+x,j+y) (3) where d(i,j) represents the edge density map. Edge-based methods are too sensitive to unwanted edges and so this method can hardly be applied to complex images. License Plate can be extracted based on the color of the plate and the characters present in it. A neural network is used for more stable color extraction[4]. To find a plate region, fixed region of horizontal and vertical length is used. A color of a pixel is extracted using Hue, Lightness and Saturation (HLS) values of eight neighboring pixels. These surrounding colors influence the perception of a pixel color. Irrelevant pixels are blended into larger color groups for smoothing. A node of maximum value is chosen as the representative color. A standard back-propagation learning algorithm is used for training and testing. Solutions based upon color do not provide a high degree of accuracy in a natural scene as color is not stable when the lighting conditions change. Though color processing shows better performance, it still has difficulties, if the image has many similar parts of color values to a plate region. Fuzzy logic can be used for locating license plates. License plates are describes using fuzzy sets bright, dark, bright and dark sequence, texture, and yellowness to get the horizontal and vertical plate position[3]. Edge of the license plate is detected from an RGB image examining object and background color pairs. It is then followed by fuzzification, generating fuzzy H,S,I maps. Fuzzy maps and edge maps are combined to extract license plates. This process is known as fuzzy aggregation. Local irregularity in the image is described using image statistics using Sliding Concentric Windows (SCW) method. In this algorithm, two concentric windows are created for the first pixel of the image. Statistical measurements in windows were calculated. If the ratio of the statistical measurements exceeds a threshold, then the central pixel of the windows is considered to belong to a Region of Interest(ROI). The size of the windows and threshold accounts for proper segmentation of license plate. 2.2 Character Segmentation Markov Network [11] can be used for segmentation and recognition of license plate characters. Plate recognition algorithm determines the position and size of the license plate in an image. Skew correction algorithm is used to align the characters horizontally in the plate. Each character is extracted using histogram of a character color. CCA technique is applied to binarized plate candidate for eliminating undesired image areas. Components not satisfying the prescribed aspect ratios are deleted. Remaining components are aligned by applying Hough Transform to the centers of gravity of components. Noise components are assumed to be smaller than characters. So, if the number of remaining components is still larger than a prescribed number, connected components are deleted one at a time starting with the smallest one. SCW[1] method is used to segment the characters present in the license plate after resizing the resultant plate image to a standard size of 75x228 pixels using bicubic interpolation method. CCA is a technique in image processing used for scanning the image and labeling its pixels into components based on pixel connectivity. Fuzzy Neural Network[7] can be employed to recognize shifted and distorted training patterns. The input pattern is fuzzified by the network and the similarities of this pattern to the learned pattern are computed. A conclusion is reached by selecting the learned pattern with the highest similarity and a non-fuzzy output is given. Probabilistic Neural Network[2] is a multilayer feed forward network used for character recognition. The PNN is trained with training patterns to derive the activation function of a neuron. 2.3 Probabilistic Neural Network PNN classifier belongs to the class of Radial Basis Function(RBF) classifiers[2]. Members of a set are assumed to belong to different classes. Using Bayes rule, posteriori probability is calculated for observable pattern. Decision is taken by examining the posteriori probability. The pattern belongs to a class for which the posteriori probability is maximum. First layer has one pattern unit for each pattern exemplar. Second layer(hidden) contains one summation unit for each class. Output layer is the decision layer used for implementing the decision rule by selecting the maximum posteriori probability. Wiener take all competitive network is used for implementing decision layer. Available 223

3 PNN has a network structure which has a Gaussian shaped characteristic. The Euclidean distances between the points representing the training patterns in feature space for each class is calculated. The average minimum distance between exemplars in class S i is given in equation (5). d avg (i)=(1/ S i ) d j (i) (i) where S i denotes the number of elements, d j denote the distance between the j th exemplar pattern and the nearest exemplar pattern in class S i. The smoothing parameter s i for class S i is assigned as given in equation (6) s i =gd avg (i) (6) where g is a constant that has been found experimentally to be between 1.1 and Thresholding Using Otsu s Method Otsu s method [10] is a histogram based method. The normalized histogram is treated as a discrete probability density function as in equation(7). p r (r q )=n q /n (7) where n is the total number of pixels in the image, n q is the number of pixels that have the intensity level r q, and L is the total number of possible intensity levels in the image. A threshold K is chosen such that C 0 is the set of pixels with levels [0,1,,k-1] and C 1 is the set of pixels with levels [k,k+1,,l-1]. Otsu s method chooses the threshold value k 2 that maximizes the between-class variance s B which is defined as given in equation (8) s B 2 =ω 0 (μ 0 -μ T ) 2 + ω 1 (μ 1 -μ T ) 2 (8) where, ω 0 = p q (r q ) ω 1 = p q (r q ) (5) 3. NUMBER PLATE IDENTIFICATION AND AUTHENTICATION The following are the various steps involved in this system: 1. RGB image is given as input and converted to Gray scale image. 2. Threshold value is initialized. 3. SCW method is applied. 4. Properties of the plate is examined for locating the plate. 5. If the plate is not located,execute from step 4 after changing the threshold value. 6. Plate is resized using bicubic interpolation. 7. SCW method is applied to the detected plate. 8. Characters are segmented. 9. Segmented characters are recognized using PNN. 10. Registration details are extracted from the database and authenticity of the vehicle is displayed. The Fig-1 shows the various steps used in this work Input RGB Image Gray Scale Image Initialize a threshold value Apply SCW method Examine the properties and locate the plate μ 0 = q p q (r q )/ ω 0 μ 1 = q p q (r q )/ ω 0 μ T = p q (r q ) Located Yes No Change the threshold value Resize the plate using bicubic interpolation A Available 224

4 A Apply SCW to the detected plate Segment the characters Recognize using PNN Extract registration details from the database and display whether the vehicle is authenticated or not Fig-1: Steps involved in authenticating a vehicle The method proposed in this paper involves repeating SCW method with varying thresholds if no license plate is located. Initial threshold is fixed using Otsu s method. After binarizing, large and small objects are neglected. Characters which are splitted are merged and joined characters are splitted by examining the size criteria. Selected characters are resized to 9 x 12 pixels and sent to probabilistic neural network for further recognition. The PNN uses 180 patterns to get trained. Input given into this PNN is a vector having 108 input values representing a character. It is compared with class clusters by examining Euclidean distances and high output is generated for the matched character. Two letter code and the corresponding state are stored in the database. Also the database contains the information regarding old and new registration series for the vehicles. Database is used for getting the registration details of the vehicle. If, additionally, vehicle details are available in the database, it can be displayed. Vehicle is subjected to permit verification and tax collection if it belongs to other states. It is considered as authenticated, if it belongs to Tamilnadu, background color of the license plate is examined. License plate of commercial vehicles has yellow background and that of private vehicles have white background. 4. INDIAN LICENSE PLATES Two types of license plates used in India are taken for experiment. For commercial vehicles, the plate has a yellow background and black numbering. For private vehicles a white background with black numbering is used. The scheme comprises two letter identification for the state in which the vehicle is registered. It is followed by a two number code to identify the district. Last four-digit number is used to uniquely identify the vehicle. When the alphabet reaches Z, the length of the prefix is increased to 2.So after TN ,the next number is TN-01 A 0001 and after TN-01 Z 9999 it is TN-01 AA 0001 and so on. TN 07 BC 1827 is a vehicle registered in Chennai, Tamilnadu. In Tamilnadu, the letter G is reserved for Government vehicles and the letter N is reserved for Government Transport Buses. Old registration series has first three characters identify the RTO district. Eg)TCT EXPERIMENTS AND RESULTS The proposed method is tested with images showing license plate clearly, images with dark license plate, images with license plate with characters of different sizes, images with tilted license plates. Vehicle category is determined according to the background color of the license plate. If the image is clear and characters are visible to our satisfaction, the proposed system works well. For detecting plates in commercial vehicles, plates is detected using Hue, Saturation and Intensity (HSI) model and further SCW and PNN are used for character segmentation and recognition respectively. The proposed system is also tested with images taken with improper illumination. PNN is trained well so that the characters are recognized properly. The presence of multiple lines in the license plate is detected using projection method. The proposed system manages to detect the plate present in the vehicle image with complex background. Vehicle is authenticated if the number detected satisfies either old or new registration series for Tamilnadu. Vehicle is subjected to permit verification and tax collection if it belongs to states, other than Tamilnadu in India. 6. CONCLUSION Sliding Concentric Windows method can be used to detect license plate by repeating with varying threshold values and Probabilistic Neural Network can be used to recognize various characters in it. Further authentication can be done by comparing the license plate number with the database stored. Vehicles belonging to other states are subjected to tax collection and permit verification. 7. FUTURE ENHANCEMENT The proposed work can be enhanced by identifying the vehicle type using image processing techniques. In this case, vehicle Available 225

5 type is detected using image processing techniques and further compared with vehicle details maintained in the database. REFERENCES [1]. C.Anagnostopoulos, I.Anagnostopoulos, E.Kayafas, and V.Loumos, A license plate recognition system for intelligent transportation system applications, IEEE Transaction for Intelligent Transportation Systems, Vol.7, no.3, pp Sep [2]. K.Bose,P.Liang, Neural Network Fundamentals with Graphs, Algorithms, and Applications, Tata McGraw-Hill Edition, [3]. S.L.Chang, L.S.Chen, Y.C.Chung and S.W.Chen, Automatic license plate recognition, IEEE Transaction for Intelliegent Transportation System,Vol.5,No.1,pp Mar [4]. Eun Ryung Lee, PyeoungKee Kim, and Hang Joon Kim, Automatic Recognition of a Car License Plate using Color Image Processing, IEEE ICIP, Vol 2, pp ,Nov [5]. B.Hongliang and L.Changping, A Hybrid license plate extraction metod based on edge statistics and morphology, ICPR,pp , 2004 [6]. S.K.Kim,D.W.Kim, and H.J.Kim, Recognition of vehicle license plate using genetic algorithm based segmentation, International conference on image processing,pp Sep [10]. Rafael Gonzalez,Richard E.Woods,Steven L.Eddins, Digital Image Processing Using MATLAB, Pearson Education,2004. [11]. Xin Fan, Guoliang Fan, Dequn Liang, Joint Segmentation and Recognition of License Plate Characters, Proc.ICIP,pp ,2007. BIOGRAPHIES Prof.T.Anusha completed her Bachelor degree in Computer Science and Engineering from Dr.Sivanthi Aditanar College of Engineering, Tiruchendur and Master degree in Computer Science and Engineering from Manonmaniam Sundaranar University, Tirunelveli. She is currently working as an Assistant Professor in the Department of Computer Science and Engineering in PSG College of Technology, Coimbatore Prof.T.Sivakumar completed his Master degree in Computer Science and Engineering from Anna University of Technology, Coimbatore. He is currently working as an Assistant Professor (Senior Grade) in the Department of Information Technology, PSG College of Technology, Coimbatore. [7]. H.Kwan and Y.Cai, A Fuzzy Neural Network and its application to pattern recognition, IEEE Transaction on Fuzzy Systems. Vol 2,no 3 pp Aug [8]. K.V.Mardia and T.J.Hainsworth, A spatial thresholding method for image segmentation, IEEE Transaction on Pattern Analysis and Machine Intelligence,Vol.10,no.6,pp , Nov [9]. N.Nasrabadi and R.King, Image coding using vector quantization:a review, IEEE Transaction on Communication, Vol 36,no.8,pp , Aug Available 226

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