Detection and Recognition of Traffic Sign using FCM with SVM

This paper mainly focuses on Traffic Sign and board Detection systems that have been placed on roads and highway. This system aims to deal with real-time traffic sign and traffic board recognition, i.e. localizing what type of traffic sign and traffic board are appears in which area of an input image at a fast processing time. Our detection module is based on proposed extraction and classification of traffic signs built upon a color probability model using HAAR feature Extraction and color Histogram of Orientated Gradients (HOG).HOG technique is used to convert original image into gray color then applies RGB for foreground. Then the Support Vector Machine (SVM) fetches the object from the above result and compares with database. At the same time Fuzzy Cmeans cluster (FCM) technique get the same output from above result and then  to compare with the database images. By using this method, accuracy of identifying the signs could be improved. Also the dynamic updating of new signals can be done. The goal of this work is to provide optimized prediction on the given sign

Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular Traffic Signs Recognition system

International audienceIn this paper, we present robust visual speed limit signs detection and recognition systems for American and European signs. Both are variants of the same modular traffic signs recognition architecture, with a sign detection step based only on shape-detection (rectangles or circles), which makes our systems insensitive to color variability and quite robust to illumination variations. Instead of a global recognition, our system classifies (or rejects) the speed-limit sign candidates by segmenting potential digits inside them, and then applying a neural network digit recognition. This helps handling global sign variability, as long as digits are properly recognized. The global sign detection rate is around 90% for both (standard) U.S. and E.U. speed limit signs, with a misclassification rate below 1%, and not a single validated false alarm in >150 minutes of recorded videos. The system processes in real-time videos with images of 640x480 pixels, at ~20frames/s on a standard 2.13GHz dual-core laptop

Detection and Recognition of Traffic Signs Inside the Attentional Visual Field of Drivers

Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver\u27s 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver

Machine learning methods for sign language recognition: a critical review and analysis.

Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction

Automatic Vehicle Detection and Identification using Visual Features

In recent decades, a vehicle has become the most popular transportation mechanism in the world. High accuracy and success rate are key factors in automatic vehicle detection and identification. As the most important label on vehicles, the license plate serves as a mean of public identification for them. However, it can be stolen and affixed to different vehicles by criminals to conceal their identities. Furthermore, in some cases, the plate numbers can be the same for two vehicles coming from different countries. In this thesis, we propose a new vehicle identification system that provides high degree of accuracy and success rates. The proposed system consists of four stages: license plate detection, license plate recognition, license plate province detection and vehicle shape detection. In the proposed system, the features are converted into local binary pattern (LBP) and histogram of oriented gradients (HOG) as training dataset. To reach high accuracy in real-time application, a novel method is used to update the system. Meanwhile, via the proposed system, we can store the vehicles features and information in the database. Additionally, with the database, the procedure can automatically detect any discrepancy between license plate and vehicles

Unconstrained Road Sign Recognition

There are many types of road signs, each of which carries a different meaning and function: some signs regulate traffic, others indicate the state of the road or guide and warn drivers and pedestrians. Existent image-based road sign recognition systems work well under ideal conditions, but experience problems when the lighting conditions are poor or the signs are partially occluded. The aim of this research is to propose techniques to recognize road signs in a real outdoor environment, especially to deal with poor lighting and partially occluded road signs. To achieve this, hybrid segmentation and classification algorithms are proposed. In the first part of the thesis, we propose a hybrid dynamic threshold colour segmentation algorithm based on histogram analysis. A dynamic threshold is very important in road sign segmentation, since road sign colours may change throughout the day due to environmental conditions. In the second part, we propose a geometrical shape symmetry detection and reconstruction algorithm to detect and reconstruct the shape of the sign when it is partially occluded. This algorithm is robust to scale changes and rotations. The last part of this thesis deals with feature extraction and classification. We propose a hybrid feature vector based on histograms of oriented gradients, local binary patterns, and the scale-invariant feature transform. This vector is fed into a classifier that combines a Support Vector Machine (SVM) using a Random Forest and a hybrid SVM k-Nearest Neighbours (kNN) classifier. The overall method proposed in this thesis shows a high accuracy rate of 99.4% in ideal conditions, 98.6% in noisy and fading conditions, 98.4% in poor lighting conditions, and 92.5% for partially occluded road signs on the GRAMUAH traffic signs dataset

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown