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

Specialized ensemble of classifiers for traffic sign recognition

Proceeding of: 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastían, España, junio, 2007.Several complex problems have to be solved in order to build Advanced Driving Assistance Systems. Among them, an important problem is the detection and classification of traffic signs, which can appear at any position within a captured image. This paper describes a system that employs independent modules to classify several prohibition road signs. Combining the predictions made by the set of classifiers, a unique final classification is achieved. To reduce the computational complexity and to achieve a real-time system, a previous input feature selection is performed. Experimental evaluation confirms that using this feature selection allows a significant input data reduction without an important loss of output accuracy.The research reported here has been supported by the Ministry of Education and Science under project TRA2004-07441-C03-C02

Traffic Sign Detection and Recognition with Voice Assistant

here are multitude of applications for detection and recognition of images across different fields. There are some specific applications for these systems used to help people to drive for example in autonomous driving as well as other applications. There has been another focus in the use of classification models used to help drivers providing details about their surrounding while driving. In places like Guadalajara, such models are a valuable tool to reduce traffic accidents. This document will explain the development of a detection and recognition of traffic signs model. This model has the intention of providing details about the meaning of the traffic signs. All this will happen close to real time and will be an additional information to the driver. This whole system could be used by anyone but specifically aimed to people with visual deficiencies. With the use of a robust machine learning and the use of Deep Learning (DL), the expectative is to achieve high accuracy levels on the traffic sign detection and recognition. This system is expected to be available and affordable for most of the drivers in Guadalajara.ITESO, A. C

Recognizing New Classes with Synthetic Data in the Loop : Application to Traffic Sign Recognition

On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio for new/known classes; even for more challenging ratios such as , the results are also very positive

An Accelerated Hierarchical Approach for Object Shape Extraction and Recognition

We present a novel automatic supervised object recognition algorithm based on a scale and rotation invariant Fourier descriptors algorithm. The algorithm is hierarchical in nature to capture the inherent intra-contour spatial relationships between the parent and child contours of an object. A set of distance metrics are introduced to go along with the hierarchical model. To test the algorithm, a diverse database of shapes is created and used to train standard classification algorithms, for shape-labeling. The implemented algorithm takes advantage of the multi-threaded architecture and GPU efficient image-processing functions present in OpenCV wherever possible, speeding up the running time and making it efficient for use in real-time applications. The technique is successfully tested on common traffic and road signs of real-world images, with excellent overall performance that is robust to moderate noise levels

Indian Traffic Signboard Recognition and Driver Alert System Using Machine Learning

Sign board recognition and driver alert system which has a number of important application areas that include advance driver assistance systems, road surveying and autonomous vehicles. This system uses image processing technique to isolate relevant data which is captured from the real time streaming video. The proposed method is broadly divided in five part data collection, data processing, data classification, training and testing. System uses variety of image processing techniques to enhance the image quality and to remove non-informational pixel, and detecting edges. Feature extracter are used to find the features of image. Machine learning algorithm Support Vector Machine(SVM) is used to classify the images based on their features. If features of sign that are captured from the video matches with the trained traffic signs then it will generate the voice signal to alert the driver. In India there are different traffic sign board and they are classified into three categories: Regulatory sign, Cautionary sign, informational sign. These Indian signs have four different shapes and eight different colors. The proposed system is trained for ten different types of sign . In each category more than a thousand sample images are used to train the network

Recognition of Traffic Signs for Car Model

Tato diplomová práce se zabývá detekcí a rozpoznáváním dopravních značek v obraze. Cílem práce bylo navrhnout program pro rozpoznávání modelů dopravních značek v reálném čase. Program má být navržen pro vestavěné zařízení postavené na platformě ARM. V první části práce budou popsány způsoby, kterými mohou být dopravní značky detekovány, a algoritmy, které se k tomu používají. Dále bude popsán používaný klasifikátor, pro klasifikaci dopravních značek. V další části práce se budu věnovat implementaci programu a použitým algoritmům. Nakonec budou vyhodnoceny výsledky, kterých se podařilo dosáhnout.This master's thesis deals with detection and recognition of traffic signs in the image. The aim of the thesis was to design a program for real-time identification of model traffic signs. The program should be designed for an embedded device on ARM based platform. The first part of the thesis will describe the ways in which traffic signs can be detected and the algorithms used for this purpose. Further, will be described the classifier used for the classification of traffic signs. In the next part I will deal with the implementation of the program and the algorithms used. Finally, the results that have been achieved will be evaluated.460 - Katedra informatikyvelmi dobř

Reconstruction of 3D Urban Scenes Using a Moving Lidar Sensor

In this report, we propose algorithms which interpret and display 3D environments.The input of this procedure is a LiDAR sensor mounted atop of a car. The sensor outputs a data stream covering more than 100 meters radius of space, collecting data at 15Hz. The recording is done in real environment on the streets of Budapest in real time, while the processing is offline, implemented on CPU keeping in mind the future implementation on GPUs to reach real time data processing. The aim is to segment several region classes (such as roads, building walls, vegetation) and to identify specified objects (such as people, vehicles, traffic signs) in the point clouds through a presegmentation step. To achieve this classification, we need several features such as the color and geometrical properties of the specified objects and their possible geometrical and physical interactions. Also, we need to take into account the time domain features calculated based on the LiDAR data stream. After this presegmentation step we are able to reconstruct building facades in 3D and to track the detected objects in the 3D space. Also, in the future, this processed data set can be registered against 2D images provided by conventional cameras to reproduce realistic, colored 3D virtua