Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

Filters

20
4 1
Reset filters

Settings
Now showing 1 - 10 of 20
  • Publication
    Self-calibration of time-based localization systems in noisy environments with nonlinear optimization
    ( 2021)
    Sidorenko, Juri
    ;
    Hugentobler, Urs
    ;
    Schindelhauer, Christian
    ;
    Dambeck, Johann
    Self-calibration is the ability of a measurement system to estimate system parameters without additional hardware. This can be the unknown coordinates of the positioning system or a fixed runtime delay caused by the hardware. In this thesis, methods are presented on how a self-calibration can be carried out in realistic environments.
  • Publication
    Deep Learning based Vehicle Detection in Aerial Imagery
    ( 2021)
    Sommer, Lars Wilko
    The usage of airborne platforms, such as unmanned aerial vehicles (UAVs), equipped with camera sensors is essential for a wide range of applications in the field of civil safety and security. Amongst others, prominent applications include surveillance and reconnaissance, traffic monitoring, search and rescue, disaster relief and environmental monitoring. However, analyzing the aerial imagery data solely by human operators is often not practicable due to the large amount of visual data and the resulting cognitive overload. In practice, automated processing chains based on appropriate computer vision algorithms are employed to assist human operators in assessing the aerial imagery data. Key component of such processing chains is an accurate detection of all relevant objects inside the camera's field of view, before the scene can be analyzed and interpreted. The low spatial resolution originating from the large distance between camera and ground makes object detection in aerial imagery a challenging task, which is further impeded by motion blur, occlusions or shadows. Although many conventional approaches for object detection in aerial imagery exist in the literature, the limited representation capacity of the utilized handcrafted features often inhibits reliable detection accuracies due to the occurring high variance in object scale, orientation, color, and shape. In the scope of this thesis, a novel deep learning based detection approach is developed, whereby the focus lies on vehicle detection in aerial imagery recorded in top view. For this purpose, Faster R-CNN is chosen as base detection framework because of its superior detection accuracy compared to other deep learning based detectors. Relevant adaptations to account for the specific characteristics of aerial imagery, especially the small object dimensions, are systematically examined and resulting issues with respect to real-world applications, i.e., the high number of false detections caused by vehicle-like structures and the poor inference time, are identified. Two novel components have been proposed to improve the detection accuracy by enhancing the contextual content of the employed feature representation. The first component aims at increasing spatial context information by combining features of shallow and deep layers to account for fine and coarse structures, while the latter component leverages semantic labeling - the pixel-wise classification of an image - to introduce more semantic context information. Two different variants to integrate semantic labeling into the detection framework are realized: exploitation of the semantic labeling results to filter out unlikely predictions and inducing scene knowledge by explicitly merging the semantic labeling network into the detection framework via shared feature representations. Both components clearly reduce the number of false detections, resulting in considerably improved detection accuracies. To reduce the computational effort and consequently the inference time, two alternative strategies are developed in the context of this thesis. The first strategy is replacing the default CNN architecture used for feature extraction with a lightweight CNN architecture optimized with regard to vehicle detection in aerial imagery, while the latter strategy comprises a novel module to restrict the search area to areas of interest. The proposed strategies result in clearly reduced inference times for each component of the detection framework. Combining the proposed approaches significantly improves the detection performance compared to the standard Faster R-CNN detector taken as baseline. Furthermore, existing approaches for vehicle detection in aerial imagery, taken from the literature, are outperformed in quantitative and qualitative manner on different aerial imagery datasets. The generalization ability is further demonstrated on a large set of previously unseen data collected from novel aerial imagery datasets with differing properties.
  • Publication
    Selbstkalibrierung mobiler Multisensorsysteme mittelsgeometrischer 3D-Merkmale
    (KIT, 2020)
    Hillemann, Markus
    Ein mobiles Multisensorsystem ermöglicht die effiziente, räumliche Erfassung von Objekten und der Umgebung. Die Kalibrierung des mobilen Multisensorsystems ist ein notwendiger Vorverarbeitungsschritt für die Sensordatenfusion und für genaue räumliche Erfassungen. Bei herkömmlichen Verfahren kalibrieren Experten das mobile Multisensorsystem in aufwändigen Prozeduren vor Verwendung durch Aufnahmen eines Kalibrierobjektes mit bekannter Form. Im Gegensatz zu solchen objektbasierten Kalibrierungen ist eine Selbstkalibrierung praktikabler, zeitsparender und bestimmt die gesuchten Parameter mit höherer Aktualität.
  • Publication
    Image-Based 3D Reconstruction of Dynamic Objects Using Instance-Aware Multibody Structure from Motion
    (KIT Scientific Publishing, 2020)
    Bullinger, Sebastian
    Computing three-dimensional reconstructions of dynamic scenes is one of the fundamental problems in computer vision. For many applications this task can be reduced to the determination of three-dimensional object motion trajectoriesw.r.t. mainly static environment structures. This approach simplifies the reconstruction problem by constraining projective ambiguities of different scene components. Image-based reconstruction approaches such as Multibody Structure from Motion (MSfM) represent an appealing choice to reconstruct dynamic scenes given suitable conditions like sufficiently textured surfaces and non-degenerated camera trajectories. The underlying assumption of MSfM is that the scene maybe represented by a multibody system, i.e., that the scene consists of multiplen on-deformable components, which may undergo independent translational and rotational displacements. Existing MSfM approaches use epipolar constraints or motion segmentation to determine component specific feature correspondences to reconstruct independently moving components. Such methods are agnostic to semantics and fail in certain scenarios like stationary or parallel moving objects. It is difficult to identify capabilities and limitations of existing approaches, because of the lack of image-based dynamic object reconstruction baseline algorithms and benchmark datasets. We propose a novel MSfM algorithm for moving object reconstruction that incorporates (instance-aware) semantic segmentation and multiple view geometry methods. The proposed MSfM pipeline includes a Multiple Object Tracking (MOT) algorithm that tracks two-dimensional object shapes on pixel level to determine object specific feature correspondences. We consider nonobject structures for the environment reconstruction. The proposed MSfM method allows the reconstruction of three-dimensional object shapes and object motion trajectories. We leverage camera poses w.r.t. object reconstructions and corresponding instance-aware semantic segmentations to determine object points consistent with image observations. The generated point clouds are suitable for object mesh computations. In order to compute a three-dimensional object trajectory we combine corresponding camera poses in the object and in the background reconstruction. We present different algorithms to reconstruct object motion trajectories in monocular and stereo image sequences. In the monocular case, three-dimensional object trajectories are defined up to scale. In order to resolve this ambiguity, we propose two different constraints to estimate the scale ratio between object and environment reconstructions. To facilitate the benchmarking of new and existing approaches, we additionally created two publicly available datasets for moving object reconstruction. The first dataset comprises real-world image sequences of a moving vehicle and a corresponding vehicle laser scan suitable for evaluation of object shapere constructions. The second dataset contains synthetic sequences of different vehicles in an urban environment. The ground truth includes vehicle shapes as well as vehicle and camera poses per frame. This dataset allows to quantitatively evaluate shape and trajectory reconstructions of moving objects. Using the created datasets, we evaluate our algorithms on outdoor scenarios of driving vehicles with challenging properties such as small object sizes, reflecting surfaces as well as illumination and view dependent appearance changes. We show that the proposed semantic constraint for object shape reconstruction produces meshes that are robust w.r.t. reflections and appearance changes. The quantitative evaluation of the trajectory reconstruction algorithms shows that the scale ambiguity of (monocular) image-based reconstructions poses a challenging problem. The usage of stereo image sequences resolves this ambiguity and results in more accurate and robust reconstructions. By quantitatively evaluating the proposed algorithms on our datasets we provide a reference for future research in the area of moving object reconstruction.
  • Publication
    Adaptivität und semantische Interoperabilität von Manufacturing Execution Systemen (MES)
    ( 2012)
    Schleipen, Miriam
    MES sind zwischen der Automatisierungs- und der Unternehmensleitebene von Änderungen in der Produktion betroffen. Darum ist ihre Adaptivität im Lebenszyklus der Produktionsanlagen erfolgskritisch. Zusätzlich agieren MES als Daten- und Informationsdrehscheibe. Daher müssen sie möglichst gut und nahtlos mit anderen Systemen zusammenarbeiten: MES müssen interoperabel werden und dabei die Semantik im Griff haben. Die vorliegende Arbeit begegnet beiden Aspekten.
  • Publication
    Optimal planning of operations for power transmission systems under uncertainty
    (VDI-Verlag, 2012)
    Zhang, Hui
    In this study, investigations with probabilistic methods are made to quantitatively evaluate the impacts of uncertain inputs on power system operation performances. Application of probabilistic analysis to power flow studies leads to the problem of probabilistic power flow (PPF). In the decision-making process for optimal dispatches, optimization approaches for optimal power flow (OPF) in the presence of uncertainty are required such that robust solutions can be derived. The chance constrained programming (CCP) approach is introduced to solve the OPF problem under uncertainty. With the help of chance constrained OPF, the expected value of the objective function will be minimized while requisite level of reliability to ensure constraints can be maintained at the same time. It provides an advisable risk-management tool in operation planning. Aspects of optimality and reliability of power system operations can be quantitatively balanced by carrying out chance constrained OPF.
  • Publication