Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Color detection and segmentation for road and traffic signs

This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.

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 ?-SVM gives better results in some case.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Influences of infrastructure and attitudes to health on value of travel time savings in bicycle journeys

In this paper we investigate how attitudes to health and exercise in connection with cycling influence the estimation of values of travel time savings in different kinds of bicycle environments (mixed traffic, bicycle lane in the road way, bicycle path next to the road, and bicycle path not in connection with the road). The results, based on two Swedish stated choice studies, suggest that the values of travel time savings are lower when cycling in better conditions. Surprisingly, the respondents do not consider cycling on a path next to the road worse than cycling on a path not in connection to the road, indicating that they do not take traffic noise and air pollution into account in their decision to cycle. No difference can be found between cycling on a road way (mixed traffic) and cycling in a bicycle lane in the road way. The results also indicate that respondents that include health aspects in their choice to cycle have lower value of travel time savings for cycling than respondents that state that health aspects are of less importance, at least when cycling on a bicycle path. The appraisals of travel time savings regarding cycling also differ a lot depending on the respondents’ alternative travel mode. The individuals who stated that they will take the car if they do not cycle have a much higher valuation of travel time savings than the persons stating public transport as the main alternative to cycling.

Reliability of GPS based traffic data: an experimental evaluation

GPS tracking of mobile objects provides spatial and temporal data for a broad range of applications including traffic management and control, transportation routing and planning. Previous transport research has focused on GPS tracking data as an appealing alternative to travel diaries. Moreover, the GPS based data are gradually becoming a cornerstone for real-time traffic management. Tracking data of vehicles from GPS devices are however susceptible to measurement errors – a neglected issue in transport research. By conducting a randomized experiment, we assess the reliability of GPS based traffic data on geographical position, velocity, and altitude for three types of vehicles; bike, car, and bus. We find the geographical positioning reliable, but with an error greater than postulated by the manufacturer and a non-negligible risk for aberrant positioning. Velocity is slightly underestimated, whereas altitude measurements are unreliable.

A Contour-based Separation of VerticallyAttached Traffic Signs

This report presents an algorithm for locating the cut points for and separatingvertically attached traffic signs in Sweden. This algorithm provides severaladvanced digital image processing features: binary image which representsvisual object and its complex rectangle background with number one and zerorespectively, improved cross correlation which shows the similarity of 2Dobjects and filters traffic sign candidates, simplified shape decompositionwhich smoothes contour of visual object iteratively in order to reduce whitenoises, flipping point detection which locates black noises candidates, chasmfilling algorithm which eliminates black noises, determines the final cut pointsand separates originally attached traffic signs into individual ones. At each step,the mediate results as well as the efficiency in practice would be presented toshow the advantages and disadvantages of the developed algorithm. Thisreport concentrates on contour-based recognition of Swedish traffic signs. Thegeneral shapes cover upward triangle, downward triangle, circle, rectangle andoctagon. At last, a demonstration program would be presented to show howthe algorithm works in real-time environment.

Color Segmentation using LVQ-Learning Vector Quantization

This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

Renar och vindkraft II : Vindkraft i drift och effekter på renar och renskötsel

Utbyggnaden av vindkraft inom renskötselområdet har ökat markant det senaste decenniet, trots att kunskapen om påverkan av vindkraftsetableringar ännu inte är fullt utredd och dokumenterad. I den här rapporten beskriver vi framförallt hur vindkraftparker i driftsfas påverkar renarna och renskötseln i tre olika områden. I Malå sameby har vi studerat kalvningsområdet kring Storliden och Jokkmokkslidens vindkraftparker. I Vilhelmina Norra sameby har vi studerat vinterbetesområdet kring Stor-Rotlidens vindkraftpark, samt Lögdeålandets betesområde med Gabrielsbergets vindkraftpark som används av Byrkije reinbetesdistrikt från Norge. För att få en helhetsbild av hur renarna använder sitt betesområde är det viktigt att studera renarnas betes- och förflyttningsmönster långsiktigt och över hela deras betesområde och inte bara inom det lokala området nära parken. Det är också viktigt att ta hänsyn till att renarnas betesutnyttjande skiftar från år till år och mellan olika årstider beroende på väderlek och andra yttre förutsättningar. Vi vill också understryka vikten av att kombinera den traditionella kunskapen från renskötarna med vedertagna vetenskapliga analysmetoder för att besvara de frågor som är viktiga för att kunna bedriva en hållbar renskötsel. Vi har undersökt renarnas användning av områdena genom att utföra spillningsinventeringar under åren 2009-2015 (endast i Malå sameby), och genom att följa renar utrustade med GPS-halsband under åren 2005-2015. Datat är insamlat före och under byggfas och under driftsfas (för Gabrielsberget finns GPS-data endast för driftsfasen). Vi har analyserat data genom att utveckla statistiska modeller för val av betesområde för varje område där vi har beräknat hur renarna förhåller sig till vindkraftparksområdet före, under och efter byggnation, och på Gabrielsberget när parken varit avstängd under 40 dagar och under drift vid olika renskötselsituationer. Genom intervjuer, möten och samtal, samt information från Gabrielsbergets vindkraftparks kontrollprogram, har vi tagit del av renskötarnas erfarenheter av hur renarnas beteende, och därmed även renskötseln, påverkats av vindkraftsutbyggnaden i respektive område. Våra resultat visar att renarna både på kalvnings- och på vinterbetesområden påverkas negativt av vindkraftsetableringarna (Tabell a). Renarna undviker att beta i områden där de kan se och/eller höra vindkraftsverken och föredrar att vistas i områden där vindkraftverken är skymda. I kalvningsområdet i Malå ökade användningen av skymda områden med 60 % under driftsfas. I vinterbetesområdet på Gabrielsberget, när renarna utfodrades i parken och kantbevakades intensivt för att stanna i parkområdet under driftsfas, ökade användningen av skymda områden med 13 % jämfört med när de inte var utfodrade och fick ströva mer fritt. Resultaten visar också att renarna minskar sin användning av området nära vindkraftparkerna. I kalvningslandet i Malå minskar renarna sin användning av områden inom 5 km från parkerna med 16-20 %. Vintertid vid Gabrielsbergets vindkraftpark undvek renarna parken med 3 km. Våra resultat visar även att renarnas betesro minskar inom en radie på 4 km från vindkraftparkerna under kalvningsperioden och tiden därefter i jämförelse med perioden före byggfas. Exakta avstånd som renarna påverkas beror på förutsättningarna i respektive område, exempelvis hur topografin ser ut eller om området är begränsat av stängsel eller annan infrastruktur. Förändringarna i habitatutnyttjande i våra studieområden blev tydligare när parkerna var centralt belägna i renarnas betesområde, som i kalvningsområdet i Malå eller i vinterbeteslandet på Gabrielsberget, medan det inte var lika tydliga effekter kring Stor-Rotlidens park, som ligger i utkanten av ett huvudbetesområde. Oftast är snöförhållandena bättre ur betessynpunkt högre upp i terrängen än nere i dalgångarna, på grund av stabilare temperatur, vind som blåser bort snötäcket och mer variation i topografin. Därför kan etablering av vindkraftparker i höglänta områden försämra möjligheten att använda sådana viktiga reservbetesområden under vintrar med i övrigt dåliga snöförhållanden, vilka blir allt vanligare i och med klimatförändringarna. Våra resultat tyder inte direkt på att renarna påverkats negativt under dåliga betesvintrar men fler år av studier behövs för att ytterligare klargöra hur vindkraft påverkar renarna under dessa vintrar. Våra studier har visat att etablering av vindkraft har konsekvenser för renskötseln under både barmarkssäsongen och under vintern, men effekterna förmodas få störst inverkan inom vinterbetesområdet där det är svårt att hitta alternativa betesområden för renarna. Under sommaren är betestillgången oftast mindre begränsad och renarna kan lättare hitta alternativa områden. En direkt konsekvens av Gabrielsbergets vindkraftpark som är placerad mitt i ett vinterbetesområde har blivit att renarna behöver tillskottsutfodras och bevakas intensivare för att de inte ska gå ut ur området. När den naturliga vandringen mellan olika betesområden störs för att renarna undviker att vistas i ett område kan det leda till att den totala tillgången till naturligt bete minskar och att man permanent måste tillskottsutfodra, alternativt minska antalet renar. Annan infrastruktur som vägar och kraftledningar påverkar också renarna. Vid Storliden och Jokkmokksliden och vid Stor-Rotliden där data samlats in innan vindkraftparken uppfördes visar våra resultat att renarna undviker de omkringliggande landsvägarna redan innan parkerna etablerades. Vid Stor-Rotliden ökar dock renarna användningen av områden nära vägarna efter att parken är byggd. På Gabrielsberget, där vi endast har data under drifttiden, är renarna närmare vägarna (även stora vägar som E4) när renskötarna minskar på kantbevakningen för att inte hålla renarna nära parken. Detta ökar naturligtvis risken för trafikolyckor och innebär att renskötarna måste bevaka dessa områden intensivare. Sist i rapporten presenterar vi förslag till åtgärder som kan användas för att underlätta arbetet för renskötseln om det är så att en vindkraftpark redan är byggd. Några exempel på åtgärder som är direkt kopplat till parken är att stänga av vägarna in i vindkraftparken för att förhindra nöjeskörning med skoter och bil under den tiden renarna vistas i området samt tät dialog mellan vindkraftsbolag och sameby angående vinterväghållningen av vägarna till och inom vindkraftparken. Andra mer regionala åtgärder för att förbättra förutsättningarna för renskötselarbetet på andra platser för samebyn, kan vara att sätta stängsel längst större vägar och järnvägar (t.ex. E4:an eller stambanan) i kombination med strategiskt utplacerade ekodukter. Detta för att underlätta och återställa möjligheterna till renarnas fria strövning och renskötarnas flytt av renar mellan olika betesområden.   

Road network and GPS tracking with data processing and quality assessment

GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.