This thesis is aimed at detection of lines of horizontal road markings from a point cloud, which was obtained using mobile laser mapping. The system works interactively in cooperation with user, which marks the beginning of the traffic line. The program gradually detects the remaining parts of the traffic line and creates its vector representation. Initially, a point cloud is projected into a horizontal plane, crating a 2D image that is segmented by a U-Net convolutional neural network. Segmentation marks one traffic line. Segmentation is converted to a polyline, which can be used in a geo-information system. During testing, the U-Net achieved a segmentation accuracy of 98.8\%, a specificity of 99.5\% and a sensitivity of 72.9\%. The estimated polyline reached an average deviation of 1.8cm.

<div>With more and more videos taken from dash cams on thousands of cars, retrieving these videos and searching for important information is a daunting task. The purpose of this work is to mine some key road and vehicle motion attributes in a large-scale driving video data set for traffic analysis, sensing algorithm development and autonomous driving test benchmarks. Current sensing and control of autonomous cars based on full-view identification makes it difficult to maintain a high-frequency with a fast-moving vehicle, since computation is increasingly used to cope with driving environment changes.</div><div><br></div><div>A big challenge in video data mining is how to deal with huge amounts of data. We use a compact representation called the road profile system to visualize the road environment in long 2D images. It reduces the data from each frame of image to one line, thereby compressing the video clip to the image. This data dimensionality reduction method has several advantages: First, the data size is greatly compressed. The data is compressed from a video to an image, and each frame in the video is compressed into a line. The data size is compressed hundreds of times. While the size and dimensionality of the data has been compressed greatly, the useful information in the driving video is still completely preserved, and motion information is even better represented more intuitively. Because of the data and dimensionality reduction, the identification algorithm computational efficiency is higher than the full-view identification method, and it makes the real-time identification on road is possible. Second, the data is easier to be visualized, because the data is reduced in dimensionality, and the three-dimensional video data is compressed into two-dimensional data, the reduction is more conducive to the visualization and mutual comparison of the data. Third, continuously changing attributes are easier to show and be captured. Due to the more convenient visualization of two-dimensional data, the position, color and size of the same object within a few frames will be easier to compare and capture. At the same time, in many cases, the trouble caused by tracking and matching can be eliminated. Based on the road profile system, there are three tasks in autonomous driving are achieved using the road profile images.</div><div><br></div><div>The first application is road edge detection under different weather and appearance for road following in autonomous driving to capture the road profile image and linearity profile image in the road profile system. This work uses naturalistic driving video data mining to study the appearance of roads, which covers large-scale road data and changes. This work excavated a large number of naturalistic driving video sets to sample the light-sensitive area for color feature distribution. The effective road contour image is extracted from the long-time driving video, thereby greatly reducing the amount of video data. Then, the weather and lighting type can be identified. For each weather and lighting condition obvious features are I identified at the edge of the road to distinguish the road edge. </div><div><br></div><div>The second application is detecting vehicle interactions in driving videos via motion profile images to capture the motion profile image in the road profile system. This work uses visual actions recorded in driving videos taken by a dashboard camera to identify this interaction. The motion profile images of the video are filtered at key locations, thereby reducing the complexity of object detection, depth sensing, target tracking and motion estimation. The purpose of this reduction is for decision making of vehicle actions such as lane changing, vehicle following, and cut-in handling.</div><div><br></div><div>The third application is motion planning based on vehicle interactions and driving video. Taking note of the fact that a car travels in a straight line, we simply identify a few sample lines in the view to constantly scan the road, vehicles, and environment, generating a portion of the entire video data. Without using redundant data processing, we performed semantic segmentation to streaming road profile images. We plan the vehicle's path/motion using the smallest data set possible that contains all necessary information for driving.</div><div><br></div><div>The results are obtained efficiently, and the accuracy is acceptable. The results can be used for driving video mining, traffic analysis, driver behavior understanding, etc.</div>

This thesis is devoted to learn detectors by object tracking in video sequence. In this thesis, we discuss methods for object tracking, object detection and online learning and possibilities of their using in sophisticated techniques, which combine object tracking and online learning detectors.

This paper deals with object detection and tracking using iterest points. Existing approaches are described here. Inovated method based on Generalized Hough transform and iterative Hough-space searching is  proposed in this paper. Generality of proposed detector is shown in various types of objects. Object tracking is designed as frame by frame detection.

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.

Implementing object detection methods for runway detection during landing approaches is limited in the safety-critical aircraft domain. This limitation is due to the difficulty that comes with verification of the design and the ability to understand how the object detection behaves during operation. During operation, object detection needs to consider the aircraft's position, environmental factors, different runways and aircraft attitudes. Training such an object detection model requires a comprehensive dataset that defines the features mentioned above. The feature's impact on the detection capabilities needs to be analysed to ensure the correct distribution of images in the dataset. Gathering images for these scenarios would be costly and needed due to the aviation industry's safety standards. Synthetic data can be used to limit the cost and time required to create a dataset where all features occur. By using synthesised data in the form of generating datasets in a simulated environment, these features could be applied to the dataset directly. The features could also be implemented separately in different datasets and compared to each other to analyse their impact on the object detections capabilities. By utilising this method for the features mentioned above, the following results could be determined. For object detection to consider most landing cases and different runways, the dataset needs to replicate real flight data and generate additional extreme landing cases. The dataset also needs to consider landings at different altitudes, which can differ at a different airport. Environmental conditions such as clouds and time of day reduce detection capabilities far from the runway, while attitude and runway appearance reduce it at close range. Runway appearance did also affect the runway at long ranges but only for darker runways.

Die vorliegende Arbeit beschäftigt sich mit der Konzeptionierung und Entwicklung eines neuartigen Fahrerassistenzsystems für Nutzfahrzeuge, welches die lichte Höhe von vor dem Fahrzeug befindlichen Hindernissen berechnet und über einen Abgleich mit der einstellbaren Fahrzeughöhe die Passierbarkeit bestimmt. Dabei werden die von einer Monokamera aufgenommenen Bildsequenzen genutzt, um durch indirekte und direkte Rekonstruktionsverfahren ein 3D-Abbild der Fahrumgebung zu erschaffen. Unter Hinzunahme einer Radodometrie-basierten Eigenbewegungsschätzung wird die erstellte 3D-Repräsentation skaliert und eine Prädiktion der longitudinalen und lateralen Fahrzeugbewegung ermittelt. Basierend auf dem vertikalen Höhenplan der Straßenoberfläche, welcher über die Aneinanderreihung mehrerer Ebenen modelliert wird, erfolgt die Klassifizierung des 3D-Raums in Fahruntergrund, Struktur und potentielle Hindernisse. Die innerhalb des Fahrschlauchs liegenden Hindernisse werden hinsichtlich ihrer Entfernung und Höhe bewertet. Ein daraus abgeleitetes Warnkonzept dient der optisch-akustischen Signalisierung des Hindernisses im Kombiinstrument des Fahrzeugs. Erfolgt keine entsprechende Reaktion durch den Fahrer, so wird bei kritischen Hindernishöhen eine Notbremsung durchgeführt. Die geschätzte Eigenbewegung und berechneten Hindernisparameter werden mithilfe von Referenzsensorik bewertet. Dabei kommt eine dGPS-gestützte Inertialplattform sowie ein terrestrischer und mobiler Laserscanner zum Einsatz. Im Rahmen der Arbeit werden verschiedene Umgebungssituationen und Hindernistypen im urbanen und ländlichen Raum untersucht und Aussagen zur Genauigkeit und Zuverlässigkeit des Verfahrens getroffen. Ein wesentlicher Einflussfaktor auf die Dichte und Genauigkeit der 3D-Rekonstruktion ist eine gleichmäßige Umgebungsbeleuchtung innerhalb der Bildsequenzaufnahme. Es wird in diesem Zusammenhang zwingend auf den Einsatz einer Automotive-tauglichen Kamera verwiesen. Die durch die Radodometrie bestimmte Eigenbewegung eignet sich im langsamen Geschwindigkeitsbereich zur Skalierung des 3D-Punktraums. Dieser wiederum sollte durch eine Kombination aus indirektem und direktem Punktrekonstruktionsverfahren erstellt werden. Der indirekte Anteil stützt dabei die Initialisierung des Verfahrens zum Start der Funktion und ermöglicht eine robuste Kameraschätzung. Das direkte Verfahren ermöglicht die Rekonstruktion einer hohen Anzahl an 3D-Punkten auf den Hindernisumrissen, welche zumeist die Unterkante beinhalten. Die Unterkante kann in einer Entfernung bis zu 20 m detektiert und verfolgt werden. Der größte Einflussfaktor auf die Genauigkeit der Berechnung der lichten Höhe von Hindernissen ist die Modellierung des Fahruntergrunds. Zur Reduktion von Ausreißern in der Höhenberechnung eignet sich die Stabilisierung des Verfahrens durch die Nutzung von zeitlich vorher zur Verfügung stehenden Berechnungen. Als weitere Maßnahme zur Stabilisierung wird zudem empfohlen die Hindernisausgabe an den Fahrer und den automatischen Notbremsassistenten mittels einer Hysterese zu stützen. Das hier vorgestellte System eignet sich für Park- und Rangiervorgänge und ist als kostengünstiges Fahrerassistenzsystem interessant für Pkw mit Aufbauten und leichte Nutzfahrzeuge. / The present work deals with the conception and development of a novel advanced driver assistance system for commercial vehicles, which estimates the clearance height of obstacles in front of the vehicle and determines the passability by comparison with the adjustable vehicle height. The image sequences captured by a mono camera are used to create a 3D representation of the driving environment using indirect and direct reconstruction methods. The 3D representation is scaled and a prediction of the longitudinal and lateral movement of the vehicle is determined with the aid of a wheel odometry-based estimation of the vehicle's own movement. Based on the vertical elevation plan of the road surface, which is modelled by attaching several surfaces together, the 3D space is classified into driving surface, structure and potential obstacles. The obstacles within the predicted driving tube are evaluated with regard to their distance and height. A warning concept derived from this serves to visually and acoustically signal the obstacle in the vehicle's instrument cluster. If the driver does not respond accordingly, emergency braking will be applied at critical obstacle heights. The estimated vehicle movement and calculated obstacle parameters are evaluated with the aid of reference sensors. A dGPS-supported inertial measurement unit and a terrestrial as well as a mobile laser scanner are used. Within the scope of the work, different environmental situations and obstacle types in urban and rural areas are investigated and statements on the accuracy and reliability of the implemented function are made. A major factor influencing the density and accuracy of 3D reconstruction is uniform ambient lighting within the image sequence. In this context, the use of an automotive camera is mandatory. The inherent motion determined by wheel odometry is suitable for scaling the 3D point space in the slow speed range. The 3D representation however, should be created by a combination of indirect and direct point reconstruction methods. The indirect part supports the initialization phase of the function and enables a robust camera estimation. The direct method enables the reconstruction of a large number of 3D points on the obstacle outlines, which usually contain the lower edge. The lower edge can be detected and tracked up to 20 m away. The biggest factor influencing the accuracy of the calculation of the clearance height of obstacles is the modelling of the driving surface. To reduce outliers in the height calculation, the method can be stabilized by using calculations from older time steps. As a further stabilization measure, it is also recommended to support the obstacle output to the driver and the automatic emergency brake assistant by means of hysteresis. The system presented here is suitable for parking and maneuvering operations and is interesting as a cost-effective driver assistance system for cars with superstructures and light commercial vehicles.

<p> This thesis presents work and simulations containing the use of Artificial Intelligence for real-time perception and real-time anomaly detection using the computer and sensors onboard an Unmanned Aerial Vehicle. One goal of this research is to develop a highly accurate, high-performance computer vision system that can then be used as a framework for object detection, obstacle avoidance, motion estimation, 3D reconstruction, and vision-based GPS denied path planning. The method developed and presented in this paper integrates software and hardware techniques to reach optimal performance for real-time operations. </p> <p>This thesis also presents a solution to real-time anomaly detection using neural networks to further the safety and reliability of operations for the UAV. Real-time telemetry data from different sensors are used to predict failures before they occur. Both these systems together form the framework behind the Intelligent UAV platform, which can be rapidly adopted for different varieties of use cases because of its modular nature and on-board suite of sensors. </p>

Au cours de la dernière décennie, la pathologie numérique a été améliorée grâce aux avancées des algorithmes d'analyse d'images et de la puissance de calcul. Néanmoins, le diagnostic par un expert à partir d'images histopathologiques reste le gold standard pour un nombre considérable de maladies notamment le cancer. Ce type d'images préserve la structure des tissus aussi proches que possible de leur état vivant. Ainsi, cela permet de quantifier les objets biologiques et de décrire leur organisation spatiale afin de fournir une description plus précise des tissus malades. L'analyse automatique des images histopathologiques peut avoir trois objectifs: le diagnostic assisté par ordinateur, l'évaluation de la sévérité des maladies et enfin l'étude et l'interprétation des mécanismes sous-jacents des maladies et leurs impacts sur les objets biologiques. L'objectif principal de cette thèse est en premier lieu de comprendre et relever les défis associés à l'analyse automatisée des images histologiques. Ensuite, ces travaux visent à décrire les populations d'objets biologiques présents dans les images et leurs relations et interactions à l'aide des statistiques spatiales et également à évaluer la significativité de leurs différences en fonction de la maladie par des tests statistiques. Après une étape de séparation des populations d'objets biologiques basée sur la couleur des marqueurs, une extraction automatique de leurs emplacements est effectuée en fonction de leur type, qui peut être ponctuel ou surfacique. Les statistiques spatiales, basées sur la distance pour les données ponctuelles, sont étudiées et une fonction originale afin de mesurer les interactions entre deux types de données est proposée. Puisqu'il a été montré dans la littérature que la texture d'un tissu est altérée par la présence d'une maladie, les méthodes fondées sur les motifs binaires locaux sont discutées et une approche basée sur une modification de la résolution de l'image afin d'améliorer leur description est introduite. Enfin, les statistiques descriptives et déductives sont appliquées afin d'interpréter les caractéristiques extraites et d'étudier leur pouvoir discriminant dans le cadre de l'étude des modèles animaux de cancer colorectal. Ce travail préconise la mesure des associations entre différents types d'objets biologiques pour mieux comprendre et comparer les mécanismes sous-jacents des maladies et leurs impacts sur la structure des tissus. En outre, nos expériences confirment que l'information de texture joue un rôle important dans la différenciation des deux modèles d'implantation d'une même maladie. / During the last decade, digital pathology has been improved thanks to the advance of image analysis algorithms and calculus power. However, the diagnosis from histopathology images by an expert remains the gold standard in a considerable number of diseases especially cancer. This type of images preserves the tissue structures as close as possible to their living state. Thus, it allows to quantify the biological objects and to describe their spatial organization in order to provide a more specific characterization of diseased tissues. The automated analysis of histopathological images can have three objectives: computer-aided diagnosis, disease grading, and the study and interpretation of the underlying disease mechanisms and their impact on biological objects. The main goal of this dissertation is first to understand and address the challenges associated with the automated analysis of histology images. Then it aims at describing the populations of biological objects present in histology images and their relationships using spatial statistics and also at assessing the significance of their differences according to the disease through statistical tests. After a color-based separation of the biological object populations, an automated extraction of their locations is performed according to their types, which can be point or areal data. Distance-based spatial statistics for point data are reviewed and an original function to measure the interactions between point and areal data is proposed. Since it has been shown that the tissue texture is altered by the presence of a disease, local binary patterns methods are discussed and an approach based on a modification of the image resolution to enhance their description is introduced. Finally, descriptive and inferential statistics are applied in order to interpret the extracted features and to study their discriminative power in the application context of animal models of colorectal cancer. This work advocates the measure of associations between different types of biological objects to better understand and compare the underlying mechanisms of diseases and their impact on the tissue structure. Besides, our experiments confirm that the texture information plays an important part in the differentiation of two implemented models of the same disease.

This work is dedicated to methods used for object detection in images. There is a summary of several approaches and algorithms to solve this matter, especially AdaBoost algorithm with its improvement, WaldBoost and several features used for object detection. Vital part of this work is dedicated to extending training datasets for classifier training and extending the current object detection framework with histogram of gradients features implementation. Integral part of this work is analysis of results by experiments evaluation.