GABOR FILTER PARAMETER OPTIMIZATION FOR LOCALIZATION STEP OF PLATE RECOGNITION SYSTEM

Transcription

1 GABOR FILTER PARAMETER OPTIMIZATION FOR LOCALIZATION STEP OF PLATE RECOGNITION SYSTEM Ozgur Altun Research and Development Engineer Faruk Can Kaya Research and Development Team Leader Turan Murat Guvenc Research and Development Engineer Abstract Localization step plays very important role for general license plate recognition systems. Most of license plate recognition systems apply different edge detection methods to help for detecting candidate plate areas on the frame. In this work, we have applied Gabor filter approach in order to perform edge detection operation on the frame. Applying Gabor filter provides us both having the best performance in the localization step and adapting the different environment conditions easily. Adjusting σ, θ and W parameters of Gabor filter provides us the optimal solution. In order to specify the optimal values for these parameters, we used the power of Genetic Algorithm. The details of the solution, obtained results and measured performances have been presented in the following sections. Keywords Gabor Filter, Genetic Algorithm, Plate Localization, LPR I. INTRODUCTION License Plate Recognition (LPR) systems are able to convert license plate of cars to text form by using image processing technology. LPR is the most commonly used in traffic surveillance systems which are applied on highways, military areas, parking areas and security important vehicle entrance systems. Also, there are number of applications such as determination of the density of the vehicles traffic, vehicle access control, detecting stolen and seized vehicle, verifying the plate and etc. Many kinds of LPR system have been designed and developed by both researchers and developers. Although each of solutions include different kinds of approach and techniques, all of LPR systems consist of the constant steps such as plate localization, character segmentation and optical character recognition. People who develop LPR systems apply different techniques and methods in these steps to increase performance of the system. The state of art of LPR systems was summarized in the [1] and [2]. License plate localization takes very important role in the LPR systems. The purpose of the localization step is to detect the existence of the plate and its location from the obtained camera view. Due to the directly affecting the accuracy of the LPR system, this step is the most important and difficult part of the system. General license plate localization algorithms are designed based on edge detection and thresholding methods to detect the plate candidate areas and find out the locations of each candidate license plate. The aim of the applying edge detection is to convert the image to binary image. At this point, the result of binary conversion can be affected by undesired effects such as blur, illumination, noises on the image and etc. Therefore, some additional methods need to be applied to get rid of these undesired effects. After obtaining the proper binary image, the plate candidate areas are detected. In our system, we have used the Gabor filter in order to generate the edge view of the processed frame. After detecting the candidate plate locations, the image of these areas is sent to character segmentation operation. Character segmentation help us to eliminate the plate areas which does not have the suitable characters. This usage provides us to find out the real plate area within the determined candidates area. Also, this step provides clean and separated characters for the real plate area. Then the last step is OCR that is used to recognize the characters. The Gabor Filter is a linear filter whose signal frequency is very similar to the human visual system. Gabor filter is widely used in image analysis applications such as texture discrimination, edge detection, face recognition and vehicle or plate recognition system by using extracted local texture information. When there are unlimited number of directions and scale cases, this technique has great advantages for analyzing image. Gabor filter acts as a band-pass filter for the local spatial frequency distribution in the texture [3]. The performance of Gabor filtering depends on the input parameters of Gabor function which are frequency, standard deviation and orientation. The estimation of this parameters maximize the results of function. So that these parameters must be set properly. In this work we have applied Genetic Algorithm (GA) to set most suitable parameters of Gabor filter function while performing localization step of license plate recognition system. After applying the GA on the parameters of Gabor filter, we obtained the best kernel to detect the edge view of the plate region on the image. Also, this techniques provide us to adapt different environment conditions. Therefore, the performance of the LPR system is affected directly in the positive direction. This paper is organized as follows. The next section includes the related works about our problem. In methodology section, brief information about Gabor filtering and performing Genetic Algorithms have been mentioned. Also the details of the developed method have been presented step by step in 26

2 details. In experimental results section, performance results for proposed algorithm and output image have been presented. After that the paper has been concluded in the next section. II. RELEATED WORK License plate localization is a tough task due to variations in size, shape, color, texture and spatial orientations of license plate regions in images. Therefore there are lots of studies about license plate localization. These studies perform different approaches base on edge statistics analysis, morphological filtering, Hough and Radon transform based, neural networks, and combination of plate features. V. Kamat et. al. addressed general problem of the detection of vehicle license plates from road scenes, for the purpose of vehicle tracking. They developed Hough transform based method to detect the vehicle and its plate. In this work, the straight lines were detected by using Hough transform. Then horizontally parallel lines are brought together as candidate plate regions. However, this method fails while detecting various license plates with varying colors and shapes due to being color and shape based[4]. In another study developed by H. Mahini et. al., some morphological operations (such as Close, Open, Bothat, Tophat), color analyzing and Sobel operator that uses to detect vertical edge images were used for localizing the license plate. The developed method is robust against illumination, shadow, scale, rotation and different weather conditions[5]. W. Jia et. al. presented a region-based algorithm for accurate license plate localization, where mean shift was utilized to filter and segment color vehicle images into candidate regions[6]. W.C. Zhang et. al. applied contour-based detection to improve the efficiency of localization accuracy by getting the imaginary part of the Gabor filters on contours. However this approach detects some false corners because of the single center frequency[7]. W. B. Liu. et. al. developed Improved Mathematical Morphology Edge Detection Algorithm (IMMEDA) which is a significant method to use in car license plate recognition system to detect the edge of the car image[8]. Besides all these, it is more efficient to analyze the image by using Gabor transform instead of edge detection or thresholding. License plate localization and license plate character segmentation problems are solved based on the Gabor transform in detection and local vector quantization in segmentation [9]. This is the first application of Gabor filters to license plate detection problems and the results of this study shows that the high precision working in detecting and locating license plate. Gabor filters have been successfully applied to various image analysis applications including edge detection [10], image coding [11], texture analysis [12][13][14], handwritten number recognition [15], face recognition [16], vehicle detection [17], and image retrieval [18]. However the parameters of Gabor filter are found on a trial and error basis. A systematic and general Evolutionary Gabor Filter Optimization (EGFO) approach using integrating Genetic Algorithms (GAs) is proposed in this study. In this way it is simple, general, and powerful framework for optimizing the parameters of Gabor filters[19]. III. METHODOLOGY In this work, Gabor filter has been used in order to determine right location of the license plate from the image. The main aim is to specify the best parameters of the Gabor filter which are frequency, standard deviation and orientation by using GA. LPR Systems are usually applied under varying environmental conditions. In order to have best performance, the configuration of the system must be updated with instantaneously varying environmental conditions factors. The illumination, height of the pole, state of the road and weather conditions are the factors affecting the LPR systems. In order to decrease the effect of these parameters and provide fast adaptation to environmental conditions, Gabor filter parameters must be optimized which are applied in the performed plate location step. As we mentioned before, detecting location of the plate image plays key role to find out the accuracy of a LPR system. Gabor filter provides us to find out plate candidates areas in the image. If the right plate is not included in the determined candidate areas, the considered vehicle can be missed or produce false results. Therefore the overall performance of the system is affected negatively. A. Gabor Filter Gabor filter family can be represented as a Gaussian function modulated by a complex sinusoidal signal[19]. In the spatial domain Gabor Filter is defined as follow: 1 g(x, y) = exp [ 1 2πσ x σ y 2 ( x σx 2 + y σy 2 )] exp [j2πw x ] (1) x = x cos θ + y sin θ (2) y = x sin θ + y cos θ (3) where x and y are the positions of light pulse in the visual field, σ x and σ y are standard deviation forms of the Gaussian term for scaling the filter, θ is the orientation of the Gabor filter and W is the radial frequency of the sinusoid. In the frequency domain Gabor Filter acts as a bandpass filter and is defined as follow: G(u, v) = exp [ 1 W )2 ((u 2 σu 2 + v2 σv 2 )] (4) where σ u = 1 2 πσ x, σ y = 1 2 πσ y. The Fourier domain representation in (4) specifies the amount by which the filter modifies each frequency component of the input image. B. Genetic Algorithm Genetic Algorithms (GAs) are a family of computational models inspired by natural selection mechanism. GAs is an iterative based algorithm which uses the past information to improve the considered parameters. GAs encode a potential solution to a specific problem on a simple chromosome-like data structure and apply recombination operators to these structures so as to preserve critical information[20]. General GAs involves four main steps which are parent selection, crossover, mutation and evaluation. Many researchers have developed different methods to improve the results of GAs. But, each developed method performs these steps commonly. Selection, crossover and mutations steps are named as genetic operators. After applying the genetic operators, a new offspring is generated. And then each offspring is evaluated according to used fitness function and new population is formed. 27

3 C. Proposed Method In our proposed method, we have applied our genetic algorithm method to Gabor filter parameters which are σ, θ and W. The details of the algorithm are given below. Fig. 1. Fig. 2. 1) In order to compare the results and calculate fitness values for each offspring we have composed an image set which include same sized images. Also we have marked the real plate area and specified the coordinate points for each image. You can see example test image on Fig.1. (a) Example image from used test images 2) We have produced 100 random different σ, θ and W values and keep them in an array structure as offspring which are represented on Fig.2. Example initial values of θ, σ and W 3) For each element produced in the second step, we have calculated fitness values according to composed image set in the first step. In order to calculate fitness value, below steps have been applied. a) Gabor function has been applied for each σ, θ and W values. b) Obtained plate candidate areas are compared with all real plate area information for each image in the test set. c) In order to calculate the fitness values, we have considered two conditions. The first condition is the distance between center points of each plate candidate areas and real center point. The second condition is the intersection ratio between candidate plate area and real plate area. 4) Select two different offsprings randomly. One of these selected nodes is named as FATHER and the other one is named as MOTHER. 5) Specify a random index number between 0 and 2 and change the values within the specified index between selected offsprings. After changing the values, two new elements are produced and one of is named as SON and the other one is named as DAUGHTER. 6) Apply mutation to SON or DAUGHTER values. (b) 7) Repeat 4, 5 and 6 th steps 50 times. After applying of all operations for each offspring, we have obtained 100 new offsprings. After this operation the total element count reached to ) For each new offspring, calculate the fitness values. 9) In order to decrease the count of new offsprings, a tournament has been applied with below steps. a) Fitness values have been calculated for each obtained new σ, θ and W values. b) We have sorted the all fitness values in descending order. c) Keep the top 5 offsprings. d) Select two distinct offsprings randomly and compare the fitness values. Keep the offspring which have the higher fitness values and remove the other. 10) After applying 9 th, the new population is achieved. 11) Repeat all operations for the new population starting from 4 th step until reached desired ration. After completing the above steps, we have achieved the optimum parameters for Gabor filter. And then we have applied this parameters to have maximum success rate. IV. EXPERIMENTAL RESULTS In this work the Gabor filter have been applied as an edge detector while performing localization step of license plate recognition system. At this point, σ, θ and W parameters of Gabor filter play very important role to find out the right place of the license plate. So that, we have performed GA to specify the optimum values of considered parameters. This solution provides us to adapt the different environmental conditions. After detecting right parameters of Gabor filter, we have applied the function on the edge detection step. The results can be shown on Fig.3. After optimizing the parameters and applying the Gabor filter with considered parameters, we measured the performance of localization step as 98%. The data set of the work has been obtained from real application fields. In order to obtain the result, the data was collected from different application fields and performed by the proposed method. After applying Gabor filter and obtaining the edges of the frame, opening and closing operations have been applied to the frame in order to mark the plate candidate area and remove the noisy edges from frame. We have developed our system by using C++ programming language and OpenCV library. In our experiment, we have measured the average CPU clock cycle performance while performing Gabor filter for a frame as Given CPU performance tests have been performed on the test environment as shown in TableI. TABLE I. TEST ENVIRONMENT CPU Intel Xeon E CPU Frequency 2.6 GHz CPU Core Count 2 Instruction Set 64-bit RAM 16 GB Operating System 64-bit Windows 7 Professional The developed system has been applied on the mobile electronic tracking system which name is named as MOBESE 28

4 Proceedings of XI Workshop de Visão Computacional October 05th 07th, 2015 the localization step. As a feature work, we will extend our work by performing other optimization algorithm techniques. Also, we will apply general published datasets in order to compare with other performed works. ACKNOWLEDGMENT We would like to present our thanks to our coworkers Selen Orali and Irem Erkus for their help while organizing the paper. Also, we would like to present our thanks to Proline Bilisim Sistemleri for providing test environment and infrastructure. (a) [1] (b) [2] [3] (c) [4] [5] [6] (d) [7] [8] [9] [10] (e) Fig. 3. Input and output Image of Gabor Filter [11] in TURKEY. All application environments are provided by infrastructure. [12] V. C ONCLUSION In this work, the Gabor filter has been applied on the localization step of license plate recognition system. Both to provide environment independent solution and to have the best performance in the localization step, we have optimized the parameters of Gabor filter. At this point σ, θ and W parameters of Gabor filter play very important role to find out the right place of the license plate. In order to specify the optimal values for these parameters, we used the power of Genetic Algorithm. We improved the relevant parameters until they reach the defined conditions and achieve the optimal values. The measured performance of the system was presented in the experimental results section. The presented results show us Gabor filter is very useful operation to adapt the different environment conditions and achieve the best performance in [13] [14] [15] [16] [17] 29 R EFERENCES C.-N. Anagnostopoulos, I. Anagnostopoulos, I. Psoroulas, V. Loumos, and E. Kayafas, License plate recognition from still images and video sequences: A survey, Intelligent Transportation Systems, IEEE Transactions on, vol. 9, no. 3, pp , Sept S. Du, M. Ibrahim, M. Shehata, and W. Badawy, Automatic license plate recognition (alpr): A state-of-the-art review, Circuits and Systems for Video Technology, IEEE Transactions on, vol. 23, no. 2, pp , Feb C. A. Basca and R. Brad, Texture Segmentation. Gabor Filter Bank Optimization Using Genetic Algorithms, EUROCON The International Conference on Computer as a Tool, no. 1, pp , [Online]. Available: V. Kamat and S. Ganesan, An efficient implementation of the Hough transform for detecting vehicle license plates using DSP S, Proceedings Real-Time Technology and Applications Symposium, H. Mahini, S. Kasaei, and F. Dorri, An Efficient Features - Based License Plate Localization Method, 18th International Conference on Pattern Recognition (ICPR 06), vol. 2, W. Jia, H. Zhang, X. He, and M. Piccardi, Mean shift for accurate license plate localization, in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, vol. 2005, 2005, pp W.-C. Zhang, F.-P. Wang, L. Zhu, and Z.-F. Zhou, Corner detection using Gabor filters, Image Processing, IET, vol. 8, no. 11, pp , Liu Wen Bo; Wang Tao, Anti-Noise Car License Plate Location Algorithm Based on Mathematical Morphology Edge Detection, Applied Mechanics & Materials, no. Issue , pp , F. Kahraman, B. Kurt, and M. Go kmen, License Plate Character Segmentation Based on the Gabor Transform and Vector Quantization. R. Mehrotra, K. R. Namuduri, and N. Ranganathan, Gabor filter-based edge detection, Pattern Recognition, vol. 25, no. 12, pp , J. G. Daugman, Complete discrete 2-D gabor transforms by neural networks for image analysis and compression, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, no. 7, pp , T. P. Weldon, W. E. Higgins, and D. F. Dunn, Efficient Gabor filter design for texture segmentation, Pattern Recognition, vol. 29, no. 12, pp , A. K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, pp , T. Hofmann, J. Puzicha, and J. M. Buhmann, Unsupervised texture segmentation in a deterministic annealing framework, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp , Y. Hamamoto, S. Uchimura, M. Watanabe, T. Yasuda, Y. Mitani, and S. Tomita, A gabor filter-based method for recognizing handwritten numerals, pp , K.-c. Chung, S. C. Kee, and S. R. Kim, Face recognition using independent component analysis og gabor filter responses, in IAPR Workshop on machine vision applications, 2000, pp Z. S. Z. Sun, G. Bebis, and R. Miller, Improving the performance of on-road vehicle detection by combining Gabor and wavelet features, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems, 2002.

5 [18] B. S. Manjunath, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp , [19] Z. Sun, G. Bebis, and R. Miller, Evolutionary Gabor filter optimization with application to vehicle detection, in IEEE International Conference on Data Mining, 2003, pp [20] M. Mitchell, An Introduction to Genetic Algorithms. Cambridge, MA, USA: MIT Press,