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%

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

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

Two algorithms for detection of mutually occluding traffic signs

The robust identification of the traffic signs represents the first and one of the most important steps in the development of a traffic sign recognition (TSR) system. Traffic signs detection usually involves a color segmentation process that uses the information related to the chromatic properties of the road signs. Since the traffic video data is captured in diverse road and weather conditions, the problem relating to traffic sign detection is quite challenging. Among several issues that need to be addressed during this processing stage, the problem generated by mutually occluding traffic signs (mutual occlusion occurs when one traffic sign partially occludes the surface of other road signs) that are attached to the same pole require special attention. In these situations the color segmentation process fails to correctly identify the regions that are associated with the traffic signs. These traffic sign detection failures compromise the performance of other stages of the TSR system and in this paper we propose two approaches that address the segmentation of mutually occluding traffic signs. The first approach uses the information associated with the inner parts of the traffic signs, while the second approach applies the watershed transform to identify the signs that have their borders in contact or are mutually occluding

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

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

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

Panorama-Based Multilane Recognition for Advanced Navigation Map Generation

Precise navigation map is crucial in many fields. This paper proposes a panorama based method to detect and recognize lane markings and traffic signs on the road surface. Firstly, to deal with the limited field of view and the occlusion problem, this paper designs a vision-based sensing system which consists of a surround view system and a panoramic system. Secondly, in order to detect and identify traffic signs on the road surface, sliding window based detection method is proposed. Template matching method and SVM (Support Vector Machine) are used to recognize the traffic signs. Thirdly, to avoid the occlusion problem, this paper utilities vision based ego-motion estimation to detect and remove other vehicles. As surround view images contain less dynamic information and gray scales, improved ICP (Iterative Closest Point) algorithm is introduced to ensure that the ego-motion parameters are consequently obtained. For panoramic images, optical flow algorithm is used. The results from the surround view system help to filter the optical flow and optimize the ego-motion parameters; other vehicles are detected by the optical flow feature. Experimental results show that it can handle different kinds of lane markings and traffic signs well

Reconnaissance d'objets multiclasses pour des applications d'aide à la conduite et de vidéo surveillance

Co-encadrement de la thèse : Bogdan StanciulescuPedestrian Detection and Traffic Sign Recognition (TSR) are important components of an Advanced Driver Assistance System (ADAS). This thesis presents two methods for eliminating false alarms in pedestrian detection applications and a novel three stage approach for TSR. Our TSR approch consists of a color segmentation, a shape detection and a content classification phase. The red color enhancement is improved by using an adaptive threshold. The performance of the K-d tree is augmented by introducing a spatial weighting. The Random Forests yield a classification accuracy of 97% on the German Traffic Sign Recognition Benchmark. Moreover, the processing and memory requirements are reduced by employing a feature space reduction. The classifiers attain an equally high classification rate using only a fraction of the feature dimension, selected using the Random Forest or Fisher's Criterion. This technique is also validated on two different multiclass benchmarks: ETH80 and Caltech 101. Further, in a static camera video surveillance application, the immobile false positives, such as trees and poles, are eliminated using the correlation measure over several frames. The recurring false alarms in the pedestrian detection in the scope of an embedded ADAS application are removed using a complementary tree filter.La détection de piétons et la reconnaissance des panneaux routiers sont des fonctions importantes des systèmes d'aide à la conduite (anglais : Advanced Driver Assistance System - ADAS). Une nouvelle approche pour la reconnaissance des panneaux et deux méthodes d'élimination de fausses alarmes dans des applications de détection de piétons sont présentées dans cette thèse. Notre approche de reconnaissance de panneaux consiste en trois phases: une segmentation de couleurs, une détection de formes et une classification du contenu. Le color enhancement des régions rouges est amélioré en introduisant un seuil adaptatif. Dans la phase de classification, la performance du K-d tree est augmentée en utilisant un poids spatial. Les Random Forests obtiennent un taux de classification de 97% sur le benchmark allemand de la reconnaissance des panneaux routiers (German Traffic Sign Recognition Benchmark). Les besoins en mémoire et calcul sont réduits en employant une réduction de la dimension des caractéristiques. Les classifieurs atteignent un taux de classification aussi haut qu'avec une fraction de la dimension des caractéristiques, selectionée en utilisant des Random Forests ou Fisher's Crtierion. Cette technique est validée sur deux benchmarks d'images multiclasses : ETH80 et Caltech 101. Dans une application de vidéo surveillance avec des caméras statiques, les fausses alarmes des objets fixes, comme les arbres et les lampadaires, sont éliminées avec la corrélation sur plusieurs trames. Les fausses alarmes récurrentes sont supprimées par un filtre complémentaire en forme d'arbre

Eigen-Gradientes contra Histograma de Orientación de Gradientes para Reconocimiento de Señalamientos Viales de Límite de Velocidad

El reconocimiento automático de señalamientos viales tiene como principales aplicaciones el inventariado de carreteras, los sistemas de asistencia al conductor y la implementación de automóviles autónomos. El reconocimiento de señalamientos viales de límite de velocidad presenta su mayor importancia debido a que, gran parte de los accidentes carreteros con consecuencias mortales ocurren mientras se maneja a gran velocidad. El presente artículo tiene por objetivo realizar la clasificación de señales de tránsito de límite de velocidad, utilizando para ello dos distintos atributos: los mapas de orientación de gradientes y los histogramas de orientación de gradientes. Los resultados experimentales muestran que ambos atributos presentan eficiencias similares, utilizando diferente número de características