Embedded system for detection, recognition and classification of traffic signs

This study concerns the development of an embedded system with low computational resources and low power consumption. It uses the NXP LPC2106 with ARM7 processor architecture, for acquiring, processing and classifying images. This embedded system is design to detect and recognize traffic signs. Taking into account the processor capabilities and the desired features for the embedded system, a set of algorithms was developed that require low computational resources and memory. These features were accomplished using a modified Freeman Method in conjunction with a new algorithm "ear pull" proposed in this work. Each of these algorithms was tested with static images, using code developed for MATLAB and for the CMUcam3. The road environment was simulated and experimental tests were performed to measure traffic signs recognition rate on real environment. The technical limitations imposed by the embedded system led to an increased complexity of the project, however the final results provide a recognition rate of 77% on road tests.Thus, the embedded system features overcome the initial expectations and highlight the potentialities of both algorithms that were developed.info:eu-repo/semantics/publishedVersio

A robust algorithm for detection and classification of traffic signs in video data

—The accurate identification and recognition of the traffic signs is a challenging problem as the developed systems have to address a large number of imaging problems such as motion artifacts, various weather conditions, shadows and partial occlusion, issues that are often encountered in video traffic sequences that are captured from a moving vehicle. These factors substantially degrade the performance of the existing traffic sign recognition (TSR) systems and in this paper we detail the implementation of a new strategy that entails three distinct computational stages. The first component addresses the robust identification of the candidate traffic signs in each frame of the video sequence. The second component discards the traffic sign candidates that do not comply with stringent shape constraints, and the last component implements the classification of the traffic signs using Support Vector Machines (SVMs). The main novel elements of our TSR algorithm are given by the approach that has been developed for traffic sign classification and by the experimental evaluation that was employed to identify the optimal image attributes that are able to maximize the traffic sign classification performance. The TSR algorithm has been validated using video sequences that include the most important categories of signs that are used to regulate the traffic on the Irish and UK roads, and it achieved 87.6% sign detection, 99.2% traffic sign classification accuracy and 86.7% overall traffic sign recognition

Traffic sign detection based on simple XOR and discriminative features

Traffic Sign Detection (TSD) is an important application in computer vision. It plays a crucial role in driver assistance systems, and provides drivers with safety and precaution information. In this paper, in addition to detecting Traffic Signs (TSs), the proposed technique also recognizes the shape of the TS. The proposed technique consist of two stages. The first stage is an image segmentation technique that is based on Learning Vector Quantization (LVQ), which divides the image into six different color regions. The second stage is based on discriminative features (area, color, and aspect ratio) and the exclusive OR logical operator (XOR). The output is the location and shape of the TS. The proposed technique is applied on the German Traffic Sign Detection Benchmark (GTSDB), and achieves overall detection and shape matching of around 97% and 100% respectively. The testing speed is around 0.8 seconds per image on a mainstream PC, and the technique is coded using the Matlab toolbox

An optimization on pictogram identification for the road-sign recognition task using svms

Pattern recognition methods are used in the final stage of a traffic sign detection and recognition system, where the main objective is to categorize a detected sign. Support vector machines have been reported as a good method to achieve this main target due to their ability to provide good accuracy as well as being sparse methods. Nevertheless, for complete data sets of traffic signs the number of operations needed in the test phase is still large, whereas the accuracy needs to be improved. The objectives of this work are to propose pre-processing methods and improvements in support vector machines to increase the accuracy achieved while the number of support vectors, and thus the number of operations needed in the test phase, is reduced. Results show that with the proposed methods the accuracy is increased 3?5% with a reduction in the number of support vectors of 50?70%

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

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

Traffic and road sign recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers' tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification.Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera's algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day.Approximately 97% successful segmentation rate was achieved using this algorithm. Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim's shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment's orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but v-SVM gives better results in some case

Automated Mapping Of Accessibility Signs With Deep Learning From Ground-level Imagery and Open Data

International audienceIn some areas or regions, accessible parking spots are not geolocalized and therefore both difficult to find online and excluded from open data sources. In this paper, we aim at detecting accessible parking signs from street view panoramas and geolocalize them. Object detection is an open challenge in computer vision, and numerous methods exist whether based on handcrafted features or deep learning. Our method consists of processing Google Street View images of French cities in order to geolocalize the accessible parking signs on posts and on the ground where the parking spot is not available on GIS systems. To accomplish this, we rely on the deep learning object detection method called Faster R-CNN with Region Proposal Networks which has proven excellent performance in object detection benchmarks. This helps to map accurate locations of where the parking areas do exist, which can be used to build services or update online mapping services such as Open Street Map. We provide some preliminary results which show the feasibility and relevance of our approach