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

Coarse-to-fine classification of road infrastructure elements from mobile point clouds using symmetric ensemble point network and euclidean cluster extraction

Classifying point clouds obtained from mobile laser scanning of road environments is a fundamental yet challenging problem for road asset management and unmanned vehicle navigation. Deep learning networks need no prior knowledge to classify multiple objects, but often generate a certain amount of false predictions. However, traditional clustering methods often involve leveraging a priori knowledge, but may lack generalisability compared to deep learning networks. This paper presents a classification method that coarsely classifies multiple objects of road infrastructure with a symmetric ensemble point (SEP) network and then refines the results with a Euclidean cluster extraction (ECE) algorithm. The SEP network applies a symmetric function to capture relevant structural features at different scales and select optimal sub-samples using an ensemble method. The ECE subsequently adjusts points that have been predicted incorrectly by the first step. The experimental results indicate that this method effectively extracts six types of road infrastructure elements: road surfaces, buildings, walls, traffic signs, trees and streetlights. The overall accuracy of the SEP-ECE method improves by 3.97% with respect to PointNet. The achieved average classification accuracy is approximately 99.74%, which is suitable for practical use in transportation network management

Deep learning for time series classification

Time series analysis is a field of data science which is interested in analyzing sequences of numerical values ordered in time. Time series are particularly interesting because they allow us to visualize and understand the evolution of a process over time. Their analysis can reveal trends, relationships and similarities across the data. There exists numerous fields containing data in the form of time series: health care (electrocardiogram, blood sugar, etc.), activity recognition, remote sensing, finance (stock market price), industry (sensors), etc. Time series classification consists of constructing algorithms dedicated to automatically label time series data. The sequential aspect of time series data requires the development of algorithms that are able to harness this temporal property, thus making the existing off-the-shelf machine learning models for traditional tabular data suboptimal for solving the underlying task. In this context, deep learning has emerged in recent years as one of the most effective methods for tackling the supervised classification task, particularly in the field of computer vision. The main objective of this thesis was to study and develop deep neural networks specifically constructed for the classification of time series data. We thus carried out the first large scale experimental study allowing us to compare the existing deep methods and to position them compared other non-deep learning based state-of-the-art methods. Subsequently, we made numerous contributions in this area, notably in the context of transfer learning, data augmentation, ensembling and adversarial attacks. Finally, we have also proposed a novel architecture, based on the famous Inception network (Google), which ranks among the most efficient to date.Comment: PhD thesi

Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)

This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Advanced driver assistance system based on computer vision using detection, recognition and tracking of road signs

Los accidentes de tráfico son un grave problema socioeconómico. Obviamente el coste humano es imposible de evaluar y el económico supone un continuo e ingente gasto de dinero por parte de los gobiernos. Se han propuesto diferentes soluciones para paliar los efectos de los accidentes, una de las cuales, los Sistemas Avanzados de Ayuda a la Conducción, son el marco en el que se encuadra el presente trabajo. Estos sistemas, como su nombre indica, asisten al conductor ofreciéndole información del entorno o actuando en determinadas circunstancias para la salvaguarda de los ocupantes del vehículo, o para facilitar la conducción. El sistema que se propone en esta tesis es una plataforma multipropósito original en su concepción, cuyo fin más inmediato es reconocer las señales de tráfico de prohibición, peligro, ceda el paso, obligación e indicación. La información obtenida de ese reconocimiento se integra dentro de un módulo de aviso al conductor. Lo que se pretende es que el conductor conozca en todo momento si está contraviniendo alguna norma de circulación derivada de una velocidad o maniobra inadecuada para el tipo de señal que se ha reconocido. Dado que este sistema está embarcado en un vehículo, deberá cumplir dos requisitos de especial importancia: funcionar en tiempo real y tanto en entorno urbano como en autopista. El primero cobra especial relevancia si se piensa en que la seguridad de los ocupantes del vehículo y de los peatones puede depender de los avisos que permitan al conductor anticiparse a un peligro. La segunda, que es una aportación original, garantiza que el sistema funcionará en vías donde la velocidad es mayor y por tanto también la probabilidad y gravedad de un posible accidente. El sistema, como se decía anteriormente, sirve al desarrollo de otras aplicaciones, como es el caso del inventariado automático de señales de tráfico, tan en auge actualmente. ______________________________________________Road traffic accidents are a serious socio-economic problem, where the cost of human life is impossible to evaluate, and cause massive and continuous government spending. Different solutions have been proposed to reduce the effects of accidents, one of which, Advanced Driver Assistance Systems, forms part of the framework which encompasses the current investigation work presented in this thesis. These systems, as their name suggest, assist the driver by providing vital information on the traffic environment or by acting under speciffic circumstances to safeguard the occupants of the vehicle, or to facilitate driving. A multitask driver assistance platform is originally presented as part of the research work of this thesis, among other tasks, it has been designed to recognize road signs in both urban and non-urban environments. The road signs that have been considered are: prohibition, danger, yield, obligation and indication. The information obtained from the road sign recognition process forms part of a complete module that advises drivers on traffic circulation requirements. The primary objective of this work has been to increase driver awareness, at all times, on the legal limitations which have established for road safety. The two areas which have been considered in this thesis are the velocity of the vehicle and the velocity of the vehicle corresponding to incorrect driving manoeuvres both of which are controlled using the information contained within road signs. The assistance platform which has been designed forms an integral part of the vehicle, thus it must satisfy two important requirements: first, provide the driver with real time information from road signs and secondly, to operate in both urban and non-urban environments. Real time information is important for the safety of drivers, passengers, and pedestrians where the information provided warns the driver well in advance of any danger so that the appropriate manoeuvres can be made to correct the speed of the vehicle. One important aspect of the work presented here is that the system has also been designed for non-urban environments, such as: national roads, toll roads and motorways where there is a higher probability of more serious and fatal accidents occurring due to the increased speed. There is a wide range of possible applications for road sign recognition systems, another area of interest which has motivated the work carried out for this thesis has been an automatic road sign inventory system. From the beginning of research in automatic road sign detection applications, many di®erent stages have been proposed, such as: signal detection, road sign recognition, and road sign tracking. The work presented in this thesis provides an in depth analysis of each of these three stages and this has allowed a more robust and complete system to be designed. In this thesis an exhaustive review is presented on color spaces and their characteristic color components which are best suited to the task of searching for road signs within an image. The technique of template matching using patterns with road signs has been optimized in this research, also included is an analysis of the most adequate models required to effciently detect each type of road sign. As part of the development of the recognition stage of the system, the two currently most important tools used in object recognition have been studied, these are: template matching and neural networks (NN). A comparative analysis of both of these techniques has been performed, where emphasis has been placed on image preprocessing to optimize the results. The final stage of this system addresses the problem of road sign tracking. The method proposed is a model based on the movement of the camera within the vehicle with respect to the road sign which is taken as the reference point. All the different stages of the system, which have been developed, form part of an experimental platform used on-board a test vehicle. To investigate the viability of this system the experimental trials have been carried out under real conditions. This methodology has been used for each stage, and the results presented corroborate the advantages and effectiveness of a multitask driver assistance platform