Color transformation for improved traffic sign detection

This paper considers large scale traffic sign detection on a dataset consisting of high-resolution street-level panoramic photographs. Traffic signs are automatically detected and classified with a set of state-of-the-art algorithms. We introduce a color transformation to extend a Histogram of Oriented Gradients (HOG) based detection algorithm to further improve the performance. This transformation uses a specific set of reference colors that aligns with traffic sign characteristics, and measures the distance of each pixel to these reference colors. This results in an improved consistency on the gradients at the outer edge of the traffic sign. In an experiment with 33, 400 panoramic images, the number of misdetections decreased by 53.6% and 51.4% for red/blue circular signs, and by 19.6% and 28.4% for yellow speed bump signs, measured at a realistic detector operating point

Automatic segmentation and reconstruction of traffic accident scenarios from mobile laser scanning data

Virtual reconstruction of historic sites, planning of restorations and attachments of new building parts, as well as forest inventory are few examples of fields that benefit from the application of 3D surveying data. Originally using 2D photo based documentation and manual distance measurements, the 3D information obtained from multi camera and laser scanning systems realizes a noticeable improvement regarding the surveying times and the amount of generated 3D information. The 3D data allows a detailed post processing and better visualization of all relevant spatial information. Yet, for the extraction of the required information from the raw scan data and for the generation of useable visual output, time-consuming, complex user-based data processing is still required, using the commercially available 3D software tools. In this context, the automatic object recognition from 3D point cloud and depth data has been discussed in many different works. The developed tools and methods however, usually only focus on a certain kind of object or the detection of learned invariant surface shapes. Although the resulting methods are applicable for certain practices of data segmentation, they are not necessarily suitable for arbitrary tasks due to the varying requirements of the different fields of research. This thesis presents a more widespread solution for automatic scene reconstruction from 3D point clouds, targeting street scenarios, specifically for the task of traffic accident scene analysis and documentation. The data, obtained by sampling the scene using a mobile scanning system is evaluated, segmented, and finally used to generate detailed 3D information of the scanned environment. To realize this aim, this work adapts and validates various existing approaches on laser scan segmentation regarding the application on accident relevant scene information, including road surfaces and markings, vehicles, walls, trees and other salient objects. The approaches are therefore evaluated regarding their suitability and limitations for the given tasks, as well as for possibilities concerning the combined application together with other procedures. The obtained knowledge is used for the development of new algorithms and procedures to allow a satisfying segmentation and reconstruction of the scene, corresponding to the available sampling densities and precisions. Besides the segmentation of the point cloud data, this thesis presents different visualization and reconstruction methods to achieve a wider range of possible applications of the developed system for data export and utilization in different third party software tools

Desenvolvemento de modelos de información de infraestructuras segundo estándares abertos e parametrización automática a partir de datos xeomáticos.

It seeks to develop procedures that allow generating information models of these structures, created from the relevant information of the point clouds obtained with these systems. For this purpose, the BIM standards for civil engineering structures, both currently available and those that will be published for the duration of the thesis, will be exploited and adopted. Information modeling techniques will be used in these standards, with the aim of obtaining a system that allows modeling the structures automatically. The models will also be made compatible with other methodologies designed for BIM, whose purpose is to take full advantage of the information available for management and maintenance tasks. Meeting these objectives, an automatic modeling system will be developed according to the BIM standards for transport infrastructures, suitable for automatic feeding from geomatic data and remote sensing, which is in turn integrable into management and maintenance systems for these types of structures of civil engineering.Esta tesis busca el desarrollo de metodologías para la exportación de la información geomática de infraestructuras de transporte, particularmente estructuras ferroviarias y carreteras, obtenida mediante tecnologías de mapeado móvil. Se busca desarrollar procedimientos que permitan generar modelos de información de estas estructuras, creados a partir de la información relevante de las nubes de puntos obtenidas con estos sistemas. Con este propósito, se explotarán y adoptarán los estándares BIM para estructuras de ingeniería civil, tanto los actualmente disponibles como aquellos que serán publicados durante la duración de la tesis. Se utilizarán técnicas de modelado de información en estos estándares, con objetivo de obtener un sistema que permita realizar un modelado de las estructuras de manera automática. Se llevará a cabo también la compatibilización los modelos con otras metodologías diseñadas para BIM, cuyo propósito es el aprovechamiento total de la información disponible para tareas de gestión y mantenimiento. Cumpliendo estos objetivos se desarrollará un sistema automático de modelado según los estándares BIM para infraestructuras de transporte, apto para su alimentación automática a partir de datos geomáticos y teledetección, el cual es a su vez integrable en sistemas de gestión y mantenimiento para este tipo de estructuras de ingeniería civil.Esta tese busca o desenvolvemento de metodoloxías para a exportación da información xeomática de infraestruturas de transporte, particularmente estruturas ferroviarias e estradas, obtida mediante tecnoloxías de mapeado móbil. A tese busca o desenvolvemento de procedementos que permitan xerar modelos de información destas estruturas, creados a partir da información relevante das nubes de puntos obtidas con estes sistemas. Con este propósito, se explotarán e adoptarán os estándares BIM para estruturas de enxeñería civil, tanto os actualmente dispoñibles como aqueles que serán publicados durante a duración da tese. Utilizaranse técnicas de modelado de información nestes estándares, con obxectivo de obter un sistema que permita realizar un modelado das estruturas de maneira automática. Levarase a cabo tamén a compatibilización dos modelos con outras metodoloxías diseñadas para BIM, cuxo propósito é o aproveitamento total da información dispoñible para tarefas de xestión e mantemento. Cumplindo estes obxectivos se desenvolverá un sistema automático de modelado segundo os estándares BIM para infraestruturas de transporte, apto para a súa alimentación automática a partir de datos xeomáticos e teledetección, o cal é a súa vez integrable en sistemas de xestión e mantemento para este tipo de estruturas de enxeñería civil

Overview of Environment Perception for Intelligent Vehicles

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area

Laserkeilausaineiston ja katunäkymäkuvien hyödyntäminen tieympäristön seurannassa

Utilization of laser scanning has increased during the past few years in many fields of applications, for example, in road environment monitoring. Mild winters, increasing rainfalls and frost are deteriorating the surface and structure of the road causing road damages. The road environment and its condition can be examined for example with laser scanning and street view images. Utilization of laser scanning data and street view images in road environment monitoring was studied in this thesis. The main focus was on the road damages and drainage. Also individual trees were detected nearby road scenes. TerraModeler and TerraScan software were used for investigations. Five different lidar datasets were used to detect road damages and drainage. Both mobile and helicopter-based lidar data were available from Jakomäki area. In Rauma case, there were two datasets collected from the helicopter but the point densities were different. In addition, to helicopter-based lidar data, there were also street view images available from BlomSTREET service in Hyvinkää case. The results between the datasets were compared. Aim was to investigate if same damages can be found from the several datasets that have different point densities. Lidar data for individual tree detection was collected by helicopter from Korppoo area. Tree locations were also measured with a tachymeter to get reference data for automatic detection. Heights of the trees were manually determined from the point cloud. Manually measured heights and locations were compared with automatically detected ones. Detection of rut depths, slopes and drainage is possible from the high point density datasets. From lower point density datasets it is not possible to detect for example rut depths. Point cloud is possible to color by slopes, which may give some information about rut locations even from lower point density datasets. Obtaining slopes and drainage accurately is also possible from lower point density data. With TerraModeler water gathering points can be obtained. Panorama pictures from BlomSTREET can be utilized for ensuring if there is a rainwater outlet or if water will gather as a puddle. Tree locations were detected in a meter accuracy with automatic method. Successful detection of tree heights and locations is dependent on many things. Successful classification of the data and creation of tree models are the most important parameters.Laserkeilaus on yleistynyt ja sitä hyödynnetään useissa eri sovelluksissa kuten esimerkiksi tiesovelluksissa. Leudot ja sateiset talvet sekä routa kuluttavat tien pintaa ja rakennetta aiheuttaen tievaurioita, jotka voivat olla vaaraksi liikenteelle. Tienkuntoa ja sen ympäristöä voidaan tarkastella esimerkiksi laserkeilausaineistojen sekä katunäkymäkuvien avulla. Työssä tutkittiin kuinka laserkeilausaineistoa ja katunäkymäkuvia voidaan hyödyntää tieympäristön seurannassa. Tutkimuksessa keskityttiin tarkastelemaan tievaurioita ja kuivatusta sekä tiealueiden läheisyydessä sijaitsevien puiden tunnistusta. Tutkimuksessa käytettiin TerraModeler ja TerraScan ohjelmistoja. Tievaurioita ja kuivatusta tutkittiin viidestä eri aineistosta kolmelta eri alueelta. Jakomäen alueelta tien ominaisuuksia tutkittiin sekä mobiili- että helikopterilaserkeilausaineistosta ja Rauman alueelta vaurioita kartoitettiin kahdesta eri helikopterilla kerätystä pistetiheyden aineistosta. Hyvinkäältä helikopterilla kerätyn laserkeilausaineiston lisäksi oli saatavilla katunäkymäkuvia BlomSTREET palvelusta. Aineistoista saatuja tuloksia vertailtiin keskenään ja tutkittiin, onko niistä mahdollista havaita samankaltaisia tuloksia. Yksittäisen puun tunnistukseen käytettiin helikopterilla kerättyä laserkeilausaineistoa Korppoon alueelta ja referenssinä aineistolle toimi maastossa mitatut puiden sijainnit. Automaattisesti määritettyjen puiden sijaintia verrattiin maastossa mitattuihin sijainteihin. Myös puiden korkeus määritettiin pistepilvestä manuaalisesti ja tätä verrattiin automaattiseen korkeuden määritykseen. Korkean pistetiheyden laserkeilausaineistoilla on mahdollista tutkia tien urautumista, tien kaltevuuksia ja kuivatusta. Matalamman pistetiheyden aineistoista ei pystytä määrittämään esimerkiksi urasyvyyksiä. Pistepilvi on mahdollista värjätä kaltevuuksien mukaan, minkä avulla urautumista voidaan havaita jossain määrin myös matalampien pistetiheyksien aineistoista. Tien kaltevuuksia ja kuivatusta pystytään havaitsemaan tarkasti jopa alhaisista pistetiheyden aineistoista. TerraModelerin avulla voidaan määrittää alueet, johon sadevesi kasautuu. BlomSTREET 360 panoraamakuvien avulla pystytään tarkastamaan onko kohdassa sadevesikaivo vai kerääntyykö vesi lammikoiksi. Yksittäisten puiden sijainnin määrittäminen onnistui noin metrin tarkkuudella, mutta sijainnin ja korkeuden määrittämisen onnistuminen on riippuvainen monesta tekijästä. Pistepilven luokittelun onnistumisen lisäksi yksi tärkeä tekijä on puiden muodoista tehdyt mallit, joiden avulla TerraScan ohjelmisto etsii yksittäisiä puita