IMPROVED LICENSE PLATE LOCALIZATION ALGORITHM BASED ON MORPHOLOGICAL OPERATIONS

Automatic License Plate Recognition (ALPR) systems have become an important tool to track stolen cars, access control, and monitor traffic. ALPR system consists of locating the license plate in an image, followed by character detection and recognition. Since the license plate can exist anywhere within an image, localization is the most important part of ALPR and requires greater processing time. Most ALPR systems are computationally intensive and require a high-performance computer. The proposed algorithm differs significantly from those utilized in previous ALPR technologies by offering a fast algorithm, composed of structural elements which more precisely conducts morphological operations within an image, and can be implemented in portable devices with low computation capabilities. The proposed algorithm is able to accurately detect and differentiate license plates in complex images. This method was first tested through MATLAB with an on-line public database of Greek license plates which is a popular benchmark used in previous works. The proposed algorithm was 100% accurate in all clear images, and achieved 98.45% accuracy when using the entire database which included complex backgrounds and license plates obscured by shadow and dirt. Second, the efficiency of the algorithm was tested in devices with low computational processing power, by translating the code to Python, and was 300% faster than previous work

Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

في السنوات الأخيرة، كان هناك تطور مستمر في مجال تطبيق المركبات وعدد المركبات التي تتحرك على الطرق في جميع أنحاء البلاد. يعتبر تحديد رقم لوحة السيارة العربية بناءً على معالجة الصور مجالًا ديناميكيًا لهذا العمل ، وتم استخدام هذه التقنية لأغراض أمنية مثل تتبع السيارات المسروقة والتحكم في الوصول إلى المناطق المحظورة. يستخدم نظام تمييز اللوحات المرورية الكاميرا الرقمية لالتقاط صورة للسيارة متضمنة لوحة المرور وتعتبر كمدخل لنظام التعرف المقترح. يتكون النظام المقترح من ثلاث مراحل، تحديد لوحة ترخيص السيارة، تقسيم الاحرف والارقام الموجودة في الصورة الاساسية الى صور صغيرة تحتوي على (حرف– رقم) كلا على حدة ، والتعرف على الأحرف، يتم تحديد لوحة الرخصة  (LP) باستخدام خوارزمية كاني في الكشف على الحواف، وقد تم استخدام Connect Component Analysis (CCA) لتقسيم الحروف⸲ وأخيرًا يتم استخدام نموذج الشبكة العصبية الاصطناعية المتعددة الطبقات للتعرف على الرموز الموجودة في كل صورة، وبالتالي يتم عرض النتائج كنص على واجهة المستخدم الرسومية. وحقق النظام المقترح أداءً إجماليًا يبلغ 96 ٪ و 97.872 ٪  في تحديد لوحات المرور المتعددة الانماط والتعرف على الرموز العربية الموجودة في اللوحات على التوالي وفي ظل ظروف مختلفة.            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions

Region-based license plate detection

Automatic license plate recognition (ALPR) is one of the most important aspects of applying computer techniques towards intelligent transportation systems. In order to recognize a license plate efficiently, however, the location of the license plate, in most cases, must be detected in the first place. Due to this reason, detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of an ALPR system, which greatly affects the recognition rate and speed of the whole system. In this paper, a region-based license plate detection method is proposed. In this method, firstly, mean shift is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. Unlike other existing license plate detection methods, the proposed method focuses on regions, which demonstrates to be more robust to interference characters and more accurate when compared with other methods. © 2006 Elsevier Ltd. All rights reserved

Biologically inspired composite image sensor for deep field target tracking

The use of nonuniform image sensors in mobile based computer vision applications can be an effective solution when computational burden is problematic. Nonuniform image sensors are still in their infancy and as such have not been fully investigated for their unique qualities nor have they been extensively applied in practice. In this dissertation a system has been developed that can perform vision tasks in both the far field and the near field. In order to accomplish this, a new and novel image sensor system has been developed. Inspired by the biological aspects of the visual systems found in both falcons and primates, a composite multi-camera sensor was constructed. The sensor provides for expandable visual range, excellent depth of field, and produces a single compact output image based on the log-polar retinal-cortical mapping that occurs in primates. This mapping provides for scale and rotational tolerant processing which, in turn, supports the mitigation of perspective distortion found in strict Cartesian based sensor systems. Furthermore, the scale-tolerant representation of objects moving on trajectories parallel to the sensor\u27s optical axis allows for fast acquisition and tracking of objects moving at high rates of speed. In order to investigate how effective this combination would be for object detection and tracking at both near and far field, the system was tuned for the application of vehicle detection and tracking from a moving platform. Finally, it was shown that the capturing of license plate information in an autonomous fashion could easily be accomplished from the extraction of information contained in the mapped log-polar representation space. The novel composite log-polar deep-field image sensor opens new horizons for computer vision. This current work demonstrates features that can benefit applications beyond the high-speed vehicle tracking for drivers assistance and license plate capture. Some of the future applications envisioned include obstacle detection for high-speed trains, computer assisted aircraft landing, and computer assisted spacecraft docking

Application of Image Processing and Three-Dimensional Data Reconstruction Algorithm Based on Traffic Video in Vehicle Component Detection

Vehicle detection is one of the important technologies in intelligent video surveillance systems. Owing to the perspective projection imaging principle of cameras, traditional two-dimensional (2D) images usually distort the size and shape of vehicles. In order to solve these problems, the traffic scene calibration and inverse projection construction methods are used to project the three-dimensional (3D) information onto the 2D images. In addition, a vehicle target can be characterized by several components, and thus vehicle detection can be fulfilled based on the combination of these components. The key characteristics of vehicle targets are distinct during a single day; for example, the headlight brightness is more significant at night, while the vehicle taillight and license plate color are much more prominent in the daytime. In this paper, by using the background subtraction method and Gaussian mixture model, we can realize the accurate detection of target lights at night. In the daytime, however, the detection of the license plate and taillight of a vehicle can be fulfilled by exploiting the background subtraction method and the Markov random field, based on the spatial geometry relation between the corresponding components. Further, by utilizing Kalman filters to follow the vehicle tracks, detection accuracy can be further improved. Finally, experiment results demonstrate the effectiveness of the proposed methods

GPU Accelerated Number Plate Localization in Crowded Situation

Number Plate Localization (NPL) has been widely used as part of Automatic Number Plate Recognition (ANPR) system. NPL method determines the accuracy of ANPR system. Although it is a mature research, the challenge stills persist especially in crowded situation where many vehicles present. Therefore, a method is proposed to localize number plate in crowded situation. The proposed NPL method uses vertical edge density to extract potential region of number plate then detect the number plate using combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). The method employs GPU to deal with multiple number plate detection, to handle multi-scale detection window, and to perform real time detection. The test result shows good results, 0.9883 value of AUC (Area Under Curve), and 0.9362 of BAC (Balance Accuracy). Moreover, potential real time detection is foreseen because total process is executed in less than 50 ms. Errors are mainly caused by background that contain letters, non-standard number plate and highly covered number plat