Mobile robot motion planning in unstructured dynamic environments is a challenging task. Thus, often suboptimal methods are employed which perform global path planning and local obstacle avoidance separately. This work introduces a holistic planning algorithm which is based on the concept of state × time lattices with variable dimensionality and multiple resolutions. The adaptive planning method makes it possible to rapidly plan local maneuvers while still pursuing global optimality.

In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analyzing the system systematically and reducing the disagreement between the model predictions and the measurements of the real processes to fulfill user defined performance criteria.

Frühwarnsysteme dienen zur möglichst frühzeitigen Information über eine sich anbahnende oder auftretende Gefahr, um Personen und Organisationen die Möglichkeit zu geben entsprechend darauf reagieren zu können. Die Konzeption eines Frühwarnsystems stellt komplexe Herausforderungen an die Systemarchitekten, hierzu liefert die vorliegende Arbeit ein Rahmenwerk für die Architektur zukünftiger Frühwarnsysteme. Early warning systems are supposed to deliver information about an emerging threat in order to allow persons and organizations to react accordingly. The design of an early warning...

Diese Arbeit beschäftigt sich mit der automatischen Erkennung komplexer Situationen in Bildfolgen im Videoüberwachungskontext. Bei der Behandlung von Daten aus natürlichen Umgebungen ergeben sich Schwierigkeiten. Diese Arbeit erweitert dazu den verwendeten Formalismus um die Behandlung von Unschärfe, fehlender Information und Komplexität, zeigt die Robustheit der Situationserkennung bei natürlichen Szenarien und stellt die generische Anwendbarkeit auch über Diskursbereichsgrenzen hinaus heraus. This work addresses the automatic detection of complex situations in image sequences in the...

This book is a collection of proceedings of the talks given at the 2016 annual joint workshop of Fraunhofer IOSB and the Vision and Fusion Laboratory (IES) by the doctoral students of both institutions. The topics of individual contributions range from computer vision, optical metrology, and machine learning to data fusion and human-machine interaction.

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis.

This book summarizes presentations delivered at the 2017 annual joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB) and the Vision and Fusion Laboratory (IES) of the Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT). At this meeting, doctoral students from both institutions reported on the status of their research and discussed topics ranging from computer vision to network security and machine learning.

The availability of video data is an opportunity and a challenge for law enforcement agencies. Face recognition methods can play a key role in the automated search for persons in the data. This work targets efficient representations of low-quality face sequences to enable fast and accurate face search. Novel concepts for multi-scale analysis, dataset augmentation, CNN loss function, and sequence description lead to improvements over state-of-the-art methods on surveillance video footage.

This work proposes a feature-based probabilistic data association and tracking approach (FBPDATA) for multi-object tracking. FBPDATA is based on re-identification and tracking of individual video image points (feature points) and aims at solving the problems of partial, split (fragmented), bloated or missed detections, which are due to sensory or algorithmic restrictions, limited field of view of the sensors, as well as occlusion situations.

This book proposes to solve the low-resolution (LR) facial analysis problem with 3D face super-resolution (FSR). A complete processing chain is presented towards effective 3D FSR in real world. To deal with the extreme challenges of incorporating 3D modeling under the ill-posed LR condition, a novel workflow coupling automatic localization of 2D facial feature points and 3D shape reconstruction is developed, leading to a robust pipeline for pose-invariant hallucination of the 3D facial texture.