VEHICLE LICENSE PLATE DETECTION ALGORITHM BASED ON STATISTICAL CHARACTERISTICS IN HSI COLOR MODEL Instructor : Dr. K. R. Rao Presented by: Prasanna Venkatesh Palani (1000660520) [email protected] With constantly increasing traffic on roads, there is a need for intelligent traffic management system. License plate (LP) detection is widely used for detecting speeding cars, security control, traffic law enforcement and electronic toll collection. License plate detection can be performed via various approaches such as [1] Vector quantization Gabor transform Optical character recognition Neural networks.INTRODUCTION License plate detection is a two step process Detecting the plate. Character recognition to identify the characters on the plate. • This project discusses a method to select automatically statistical threshold value in HSI color space for detecting candidate regions. • This will lead to a framework which unifies detection, tracking and recognition of license plates.RGB AND HSI MODEL RGB Model • In the RGB color model, different colors can be reproduced by additively combining red, green, and blue in different ways. In a general sense, the RGB color model describes our perception of color. Three types of receptors in the retina of the human eye have peak sensitivities corresponding to these three primary colors. (Fig 1). • The RGB color model represents colors within a cubic volume defined by orthogonal Red, Green, and Blue axes. • Black is at the origin of the coordinate system (R=G=B=0). • White is at the opposite corner of the cube (R=G=B=255). • The diagonal connecting the black and white corners (gray dashed line) contains the range of neutral gray levels.HSI Model • The HSI color model, represents colors within a double-cone space. (Fig 2). • The vertical axis is intensity, which represents variations in the lightness and darkness of a color. • The 0 intensity level is black; full intensity is white. • HSI values elsewhere along the intensity axis represent different levels of gray. • On any horizontal slice through the model space, the hue (or “color” of the color) varies around the slice, and the saturation (the purity of the color) increases radially outward from the central intensity axis. • In the HSI color model, intensity makes no contribution to the color.ALGORITHM FOR DETECTING LICENSE PLATE REGION (FIG.3) • The algorithm for license plate detection consists of three parts – Candidate regions are identified using HSI color model – The geometrical properties of the license plate such as area, bounding box, aspect ratio, are used to filter the candidate region. – The candidate region is determined by decomposing the predetermined alphanumeric character.STEPS FOR LICENSE PLATE DETECTION As shown in Fig 4, the license plate detection involves the following steps a) Input image b) Color segmentation result c) Detected candidate after filtering d) Candidate region detection.COLOR SEGMENTATION 1) Input Image (RGB) is converted to HSI color model through the following transformation operations.[1][6] (Fig .5)RGB-HSI CONVERSIONCOLOR SEGMENTATION-BINARIZATION Binarization results in a image whose pixels have only two possible values,0 (black) and 1 (white) (Fig 6). The thresholding method used here is the Otsu algorithm [12][13], which assumes that the image to be thresholded contains two classes of pixels then calculates the optimum threshold separating those two classes so that their combined spread is minimal.MORPHOLOGICAL PROCESSING After segmentation, there may be some noise in the image such as small holes in the candidate regions. This could be resolved with morphological processing. Mathematical morphology operations are based on the shape in the image and not pixel intensities. There are two basic morphological operations [5] Dilation. Erosion. Dilation allows objects to expand while erosion shrinks the objects by eroding the boundaries. These operations can be modified by proper choice of the structuring element which determines how many objects will be dilated or eroded. Structuring element is simply a matrix of 0s and 1s that could be of any arbitrary shape and size.MORPHOLOGICAL PROCESSING In MATLAB one can define neighborhood of desired size for the structuring element such as square, rectangle, diamond etc. Preferably rectangle is used as the neighborhood for the structuring element of size 6x4. In the project, closing operation is used which is dilation followed by erosion. Removal of small holes plays an important role in obtaining the rectangular license plate. Figure 7 shows morphological operation on binary image.EDGE DETECTION, LABELING AND FILTERING Edge detection is one of the important tasks for digital image processing. Edge points possess high gradient difference in the local neighborhood. They are used for feature extraction in image processing. Considerably the image date is reduced after edge detection. Some popular edge detectors are [7] , Sobel. Robert’s Cross . Prewitt. Kirsch. Laplace. Marr-Hildreth. Edges are defined as intensity gradients within the image.EDGE DETECTION, LABELING AND FILTERING Sobel Operator In this project, Sobel edge detection operator is performed. Sobel operator is a combination of two operators one which can detect horizontal edges the other which can detect vertical edges. It is a 3x3 neighborhood based gradient operator. The result of the edge image generated by Sobel operator is shown in Fig 8EDGE DETECTION, LABELING AND FILTERING After detecting the candidate regions using color segmentation and edge detection, features of the region are to be extracted to differentiate LP regions from others. The next step of the algorithm is to label each of the connected components. Before that the image is smoothened so as to reduce the number of connected components (Fig 9). To smooth a image is to create an approximate image that attempts to capture important patterns in the data, while leaving
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