Kang-Hyun Jo

Vehicle License Plate Detection Method Based on Sliding Concentric Windows and Histogram

Detecting the region of a license plate is the key component of the vehicle license plate recognition (VLPR) system. A new method is adopted in this paper to analyze road images which often contain vehicles and extract LP from natural properties by finding vertical and horizontal edges from vehicle region. The proposed vehicle license plate detection (VLPD) method consists of three main stages: (1) a novel adaptive image segmentation technique named as sliding concentric windows (SCWs) used for detecting candidate region; (2) color verification for candidate region by using HSI color model on the basis of using hue and intensity in HSI color model verifying green and yellow LP and white LP, respectively; and (3) finally, decomposing candidate region which contains predetermined LP alphanumeric character by using position histogram to verify and detect vehicle license plate (VLP) region. In the proposed method, input vehicle images are commuted into grey images. Then the candidate regions are found by sliding concentric windows. We detect VLP region which contains predetermined LP color by using HSI color model and LP alphanumeric character by using position histogram. Experimental results show that the proposed method is very effective in coping with different conditions such as poor illumination, varied distances from the vehicle and varied weather.

Automatic Detection and Recognition of Traffic Signs using Geometric Structure Analysis

This paper proposes detection and recognition algorithm for restricting, warning and information road signs. Sign detection is based on color analysis. In actual, traffic signs have specific color information like red border for warning and restricting signs or blue background for information signs. However in images obtained by camera mounted in the car color information was changed due to lighting and weather conditions such as dark illumination, rainy and foggy weather etc. To solve that problem we use RGB color segmentation with two restriction rules: first rule is bounding constraints for each color component which provides good detection results in images with good lighting condition; second rule is using normalized color information and allows sign detection in dark images. Structure of information signs is differs from structure of warning and restricting signs hence recognition process is also different. The meaning of traffic sign lies in shape of symbols inside of it. Recognition process is based on shape analysis. For warning and restricted signs recognition process consists of two stages. We extract sign candidate from image and classify sign as a circle or triangle using background shape histograms. Then we convert the inner part of sign into binary mask and apply template matching algorithm. To understand the meaning of information sign we separate it into basic components: arrows and text, and then analyze positional relationship between those segments. Detection of arrowheads is based on morphological operations such and analysis of spatial features like area and direction. Result of recognition is name of sign for warning and restricting signs and set of pairs direction - place for information signs

HSI color based vehicle license plate detection

Vehicle license plate recognition (VLPR) is one of the most important topics of using computer vision and pattern recognition in intelligent transportation systems. In order to recognize a license plate (LP) expeditiously, the location of the LP in most cases, must be detected in the initial step. For this reason, detecting the exact and perfect location of a LP from a vehicle image is considered to be the most important and crucial step of a VLPR system, which greatly affects the recognition process and directly influences the accuracy and speed of entire system. In this paper a HSI color based license plate detection method is proposed. In this method, (a) HSI color model is used for detecting candidate regions and (b) vehicle license plate (VLP) regions are verified and detected by using position histogram. In the proposed method, input vehicle images are converted into HSI color images. Then the candidate regions are found by HSI color model on the basis of using hue, saturation and/or intensity. These candidate regions may include LP regions; geometrical properties of LP are then used for classification. Finally, VLP regions containing predetermined LP alphanumeric character are verified and detected by using position histogram. The proposed method is very effective in coping with different conditions such as poor illumination and varied weather comparing with traditional approaches. Experimental results show that the distance from the vehicle varied according to the camera setup.

Image-based Structural Analysis of Building using Line Segments and their Geometrical Vanishing Points

This paper describes an approach to detect and analyze the properties of building in image. We use line segments and belongings in the appearance of building as geometrical and physical properties respectively. The geometrical properties are represented as principal component parts (PCPs) as a set of door, window, wall and so on. As the physical properties, color, intensity, contrast and texture of regions are used. Analysis process is started by detecting straight line segments. We use MSAC to group such parallel line segments which have a common vanishing point. We calculate one dominant vanishing point for vertical direction and five dominant vanishing points in maximum for horizontal direction. A mesh of basic parallelograms is created by one of horizontal groups and vertical group. Each mesh represents one face of building. The PCPs are formed by merging neighborhood of basic parallelograms which have similar colors. The wall regions of PCPs are detected. Finally, the structure of building is described as a system of hierarchical features. The building is represented by number of faces. Each face is regarded by a color histogram vector. The color histogram vector just is computed by wall region of face. The proposed approach was used to recognize a database containing 1005 images and 115 queried images. It has been confirmed with various kinds of images taken for different conditions like camera systems, weather and seasons

Vehicle detection using tail light segmentation

This paper presents a method for vehicle detection based on forward looking CCD camera, where vehicle tail light information is employed to generate vehicle candidate. Color segmentation consists of finding pairs of light blobs and removing the isolated points after morphological closing and opening. Among the horizontal light pairs, it determines to define the vehicle candidate. In vehicle candidate verification step, a feature set by Gabor filters using eight direction and five scales is used to train a back propagation neural network (BPNN). In the experiment, this BPNN classifier is used to detect the vehicle. Total 104 images are tested by this algorithm. 87 vehicle images are detected successfully. These results show that our proposed method is effective for vehicle detection in the daytime.

Fast human detection based on parallelogram haar-like features

Inspired by a recent image descriptors for object detection, this paper proposed the feature description method based on set of modified Haar-like features which have parallelogram shapes. Using the proposed feature descriptors to develop a rapid detection system for human detection based on cascade structure used for boosting classifier. Specially, human detection in omnidirectional image as well as unwrap omnidirectional to panoramic image were described in this paper. The experimental results showed that the proposed method could produce high accuracy detection rate with lower false positive rate and higher recall rate than Haar-like features, and faster than HOG feature. It is efficiency with different resolutions and poses under a variety conditional such as flare illumination, clutter backgrounds, and so on.

Facet-based multiple building analysis for robot intelligence

This paper describes an approach to segment and recognize multiple buildings in the urban environment for robot intelligence. By grouping line segments which coincide with a common vanishing point, the non-building and building images are distinguished. The facets of building are detected and represented by the meshes of skewed parallelograms. The doors, wall region and windows are then estimated by merging the skewed parallelograms with similar color. To recognize a test image, each facet is described by its area, wall color histogram and a list of scale invariant feature transform (SIFT) descriptors. We selected a small number of SIFT features adapted with visual properties of buildings to represent the facet. To analyze multiple buildings, maximum numbers of dominant vanishing points are calculated for vertical and horizontal directions are one and five, respectively. In the first experiment, a set of 880 images is classified into building and non-building images. The second experiment is for recognizing a set of 80 test images from 500 image database. All images were taken from more than 100 buildings in Ulsan metropolitan city in South Korea under different conditions like viewpoints, camera systems, weather and seasons. We obtain 97% and 97.5% rate of correct segmentation and recognition, respectively.

Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues

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Moving object localization using optical flow for pedestrian detection from a moving vehicle.

This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells 14 × 14 pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.

Color-based Face Detection using Combination of Modified Local Binary Patterns and embedded Hidden Markov Models

This paper presents an improved face detection method for color images. We propose a boosted skin-color model in RGB space which can reduce more effectively noises forming from similar skin colors. With our solution, we receive more reasonable skin detection for different human races. We modifed local binary pattern (LBP) by adding a set of spatial templates. This LBP considers both principal local shapes and spatial textures of facial components. Human face is represented by LBP histogram. Moreover, the grayscale image of human face is changed to discrete cosine transform (DCT) coefficients used in embedded hidden Markov models (eHMMs). A modified LBP (mLBP) histogram matching and eHMMs are composed to hierarchical classifier to determine whether skin regions are faces or not. The experiments show that our method performs a better capability for face detection in complex environments than using separately eHMMs or LBP histogram. The correct face detection rate of proposed system is over 94% among our test database which consists totally 485 single and multi-face color images of 1429 persons in different lighting conditions, face rotations, occlusions and complex backgrounds from different sources: Caltech face database, Sumgmug image library, family photos, personal digital images and World Wide Web