Sebastian Houben

Sebastian Houben
Hochschule Bonn-Rhein-Sieg · Department of Computer Science

Doctor of Engineering

About

63
Publications
32,091
Reads
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1,243
Citations
Citations since 2016
42 Research Items
1061 Citations
2016201720182019202020212022050100150200
2016201720182019202020212022050100150200
2016201720182019202020212022050100150200
2016201720182019202020212022050100150200
Additional affiliations
October 2017 - March 2020
Ruhr-Universität Bochum
Position
  • Professor (Assistant)
October 2015 - September 2017
University of Bonn
Position
  • PostDoc Position
October 2010 - September 2015
Ruhr-Universität Bochum
Position
  • PhD Student

Publications

Publications (63)
Conference Paper
Full-text available
The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications can benefit from forecasts with a high temporal resolution to address sudden fluctuations of PV-pow...
Book
Full-text available
This book addresses readers from both academia and industry, since it is written by authors from both academia and industry. Accordingly, it takes on diverse viewing angles, but keeps a clear focus on machine-learned environment perception in autonomous vehicles. Special interest is on deep neural networks themselves, their robustness and uncertain...
Chapter
Full-text available
Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization...
Preprint
Full-text available
When training data is scarce, the incorporation of additional prior knowledge can assist the learning process. While it is common to initialize neural networks with weights that have been pre-trained on other large data sets, pre-training on more concise forms of knowledge has rather been overlooked. In this paper, we propose a novel informed machi...
Preprint
Full-text available
The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to...
Conference Paper
Full-text available
Data augmentation techniques have been focused in recent research as they hold the promise to reduce the need for extensive data acquisition and to enable systematic sampling, e.g., in order to examine underrepresented cases. The question of how and to what extent control over the result is possible and necessary is still open. We propose a novel s...
Conference Paper
Full-text available
The utilization of automatically generated image training data is a feasible way to enhance existing datasets, e.g., by strengthening underrepresented classes or by adding new lighting or weather conditions for more variety. Synthetic images can also be used to introduce entirely new classes to a given dataset. In order to maximize the positive eff...
Preprint
Full-text available
Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since simulations are prone to domain shift w.r.t. real-life data, it is crucial to verify the transferability of the obtained results. We propose a novel framework consisting of...
Conference Paper
Full-text available
Data-driven sensor interpretation in autonomous driving can lead to highly implausible predictions as can most of the time be verified with common-sense knowledge. However, learning common knowledge only from data is hard and approaches for knowledge integration are an active research area. We propose to use a partly human-designed, partly learned...
Preprint
Full-text available
This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability. The complexity of machine learning models has elicited research to make them more explainable. However, most explainability methods cannot provide insight beyond the given data, requiring additional information about the...
Preprint
Full-text available
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. Cyber-physical systems employing DNN...
Preprint
Full-text available
An important pillar for safe machine learning (ML) is the systematic mitigation of weaknesses in neural networks to afford their deployment in critical applications. An ubiquitous class of safety risks are learned shortcuts, i.e. spurious correlations a network exploits for its decisions that have no semantic connection to the actual task. Networks...
Preprint
Full-text available
Data-driven sensor interpretation in autonomous driving can lead to highly implausible predictions as can most of the time be verified with common-sense knowledge. However, learning common knowledge only from data is hard and approaches for knowledge integration are an active research area. We propose to use a partly human-designed, partly learned...
Conference Paper
Full-text available
Video-based traffic sign recognition is a key ability of autonomous vehicles but a demanding challenge due to the enormous number of classes and natural conditions in the wild. We address this problem with a fully automatic close-to-life image-to-image translation technique for traffic sign substitution in natural images (cf. Fig. 1). The work is i...
Chapter
The use of neural networks in perception pipelines of autonomous systems such as autonomous driving is indispensable due to their outstanding performance. But, at the same time their complexity poses a challenge with respect to safety. An important question in this regard is how to substantiate test sufficiency for such a function. One approach fro...
Preprint
Full-text available
Monte Carlo (MC) dropout is one of the state-of-the-art approaches for uncertainty estimation in neural networks (NNs). It has been interpreted as approximately performing Bayesian inference. Based on previous work on the approximation of Gaussian processes by wide and deep neural networks with random weights, we study the limiting distribution of...
Conference Paper
Full-text available
Position estimation of multiple objects in a 3D environment poses a challenging task, even more so in the presence of occlusions due to infrastructure. In this paper we present a method to accurately localize up to 10 moving pedestrians by fusing the output of a Sparsity Driven Detector with volumes generated by a Shape-from-Silhouette approach. We...
Conference Paper
Full-text available
Video-based traffic sign recognition poses a highly challenging problem due to the significant number of possible classes and large variances of recording conditions in natural environments. Gathering an appropriate amount of data to solve this task with machine learning techniques remains an overall issue. In this study, we assess the suitability...
Article
Full-text available
Accurately self-localizing a vehicle is of high importance as it allows to robustify nearly all modern driver assistance functionality, e.g., lane keeping and coordinated autonomous driving maneuvers. We examine vehicle self-localization relying only on video sensors, in particular, a system of four fisheye cameras providing a view surrounding the...
Article
Full-text available
Inspection of industrial chimneys and smoke pipes induces high costs due to production downtimes and imposes risks to the health of human workers due to high temperatures and toxic gases. We aim at speeding up and automating this process with multicopter micro aerial vehicles. To acquire high quality sensor data, flying close to the walls of the ch...
Article
Full-text available
The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception,...
Preprint
Full-text available
The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state-of-the-art in autonomous operation of ground-based and flying robots. This article covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception...
Conference Paper
Full-text available
Finding the parameters of a vignetting function for a camera currently involves the acquisition of several images in a given scene under very controlled lighting conditions, a cumbersome and error-prone task where the end result can only be confirmed visually. Many computer vision algorithms assume photoconsistency, constant intensity between scene...
Chapter
Kann man den menschlichen Beobachter sozialen Verhaltens durch Algorithmen ersetzen? Diese Frage zielt auf eine mögliche Anwendung des Computersehens zur automatischen Analyse von Videomaterial zur Überwachung. Hierbei sollen Ereignisse ohne menschlichen Beobachter erkannt werden, um eventuell Menschen zu alarmieren oder Material zur genaueren Anal...
Article
Full-text available
Robust and fast motion estimation and mapping is a key prerequisite for autonomous operation of mobile robots. The goal of performing this task solely on a stereo pair of video cameras is highly demanding and bears conflicting objectives: on one hand, the motion has to be tracked fast and reliably, on the other hand, high-level functions like navig...
Conference Paper
Most modern computer vision techniques rely on large amounts of meticulously annotated data for training and evaluation. In close-to-market development, this demand is even higher since numerous common and—more important—less common situations have to be tested and must hence be covered datawise. However, gathering the necessary amount of data read...
Article
Full-text available
The past years have shown a remarkable growth in use-cases for micro aerial vehicles (MAVs). Conceivable indoor applications require highly robust environment perception, fast reaction to changing situations, and stable navigation, but reliable sources of absolute positioning like GNSS or compass measurements are unavailable during indoor flights....
Conference Paper
Full-text available
The ability to identify, follow, approach, and intercept a non-stationary target is a desirable capability of autonomous micro aerial vehicles (MAV) and puts high demands on reliable target perception, fast trajectory planning, and stable control. We present a fully autonomous MAV that lands on a planar platform mounted on a ground vehicle, relying...
Conference Paper
Full-text available
Picking and transporting objects in an outdoor environment with multiple lightweight MAVs is a demanding task. The main challenges are sudden changes of flight dynamics due to altered center of mass and weight, varying lighting conditions for visual perception, and coordination of the MAVs over unreliable wireless connections. At the Mohamed Bin Za...
Conference Paper
Full-text available
Modern consumer RGB-D cameras are affordable and provide dense depth estimates at high frame rates. Hence, they are popular for building dense environment representations. Yet, the sensors often do not provide accurate depth estimates since the factory calibration exhibits a static deformation. We present a novel approach to online depth calibratio...
Conference Paper
Full-text available
Inspection of industrial chimneys and smoke pipes induces high costs due to production downtimes and imposes risks to the health of human workers due to high temperatures and toxic gases. We aim at speeding up and automating this process with sensors mounted on multicopters. To acquire high quality sensor data, flying close to the walls of the chim...
Article
Full-text available
Modern vehicles are deployed with a large number of sensors in order to provide a rich spectrum of driver assistance functionality. These systems enhance security and comfort of passengers and other traffic participants alike, but they also pave the road to fully autonomous traffic. In order to provide this functionality robustly and reliably, one...
Conference Paper
Full-text available
Visual SLAM is an area of vivid research and bears countless applications for moving robots. In particular, micro aerial vehicles benefit from visual sensors due to their low weight. Their motion is, however, often faster and more complex than that of ground-based robots which is why systems with multiple cameras are currently evaluated and deploye...
Conference Paper
Full-text available
Laser scanners have been proven to provide reliable and highly precise environment perception for micro aerial vehicles (MAV). This oftentimes makes them the first choice for tasks like obstacle avoidance, close inspection of structures, self-localization, and mapping. However, artificial environments may pose problems if the scene is self-similar...
Article
In order to render software viable for highly safety-critical applications, we describe how to incorporate fault tolerance mechanisms into the real-time programming language PEARL. Therefore, we present, classify, evaluate and illustrate known fault tolerance methods for software. We link them together with the requirements of the international sta...
Thesis
Full-text available
Heutige Fahrzeuge sind mit einer hohen Anzahl an Sensoren ausgestattet, um eine breites Spektrum an Fahrerassistenzfunktionen anbieten zu können. Die vorliegende Arbeit untersucht die Eignung eines neuen Kamerasystems, das kürzlich Verbreitung in den Fahrzeugen der meisten großen Automobilhersteller gefunden hat, für alle wesentlichen videobasierte...
Article
Partly or fully autonomously driving vehicles constitute one of the greatest challenges of contemporary technology. Despite the advances achieved by automobile manufacturers so far, a lack of functional safety and reliability is observable, especially if this technology is used by untrained and possibly naive operators, as intended in everyday use....
Article
We illustrate a multiple-camera surveillance system installed in a parking garage to detect arbitrary moving objects. Our system is real-time capable and computes precise and reliable object positions. These objects are tracked to warn of collisions, e.g. between vehicles, pedestrians or other vehicles. The proposed system is based on multiple gray...
Conference Paper
Full-text available
The variety of vehicle-mounted sensors in order to fulfill a growing number of driver assistance tasks has become a substantial factor in automobile manufacturing cost. We present a stereo distance method exploiting the overlapping field of view of a multi-camera fisheye surround view system, as they are used for near-range vehicle surveillance tas...
Conference Paper
Full-text available
Intrinsic calibration, i.e. finding the mapping between a camera’s image positions and corresponding view rays, is a cumbersome, yet unavoidable task in order to accurately generate and interpret results from many kinds of image processing algorithms. We address this problem in the context of vehicle-mounted cameras with arbitrary fields of view wi...
Conference Paper
In this study, we present a new indoor positioning and environment perception system for generic objects based on multiple surveillance cameras. In order to assist highly automated driving, our system detects the vehicle's position and any object along its current path to avoid collisions. A main advantage of the proposed approach is the usage of c...
Conference Paper
Full-text available
The search for free parking space in a crowded car park is a time-consuming and tedious task. Today’s park assistance systems provide the driver with acoustic or visual feedback when approaching an obstacle or semi-autonomously navigate the vehicle into the parking lot. However, finding a free parking lot is usually left to the driver. In this pape...
Conference Paper
Full-text available
Real-time detection of traffic signs, the task of pinpointing a traffic sign's location in natural images, is a challenging computer vision task of high industrial relevance. Various algorithms have been proposed, and advanced driver assistance systems supporting detection and recognition of traffic signs have reached the market. Despite the many c...
Conference Paper
Full-text available
Even for experienced drivers handling a roll trailer with a passenger car is a difficult and often tedious task. Moreover, the driver needs to keep track of the trailer’s driving stability on unsteady roads. There are driver assistance systems that can simplify trajectory planning and observe the oscillation amplitude, but they require additional h...
Conference Paper
Full-text available
For the development of vision-based driver assistance systems, large amounts of data are needed, e.g., for training machine learning approaches, tuning parameters, and comparing different methods. There are basically three possible ways to obtain the required data: using freely available benchmark sets, doing own recordings, or falling back to synt...
Article
Biological applications like vesicle membrane analysis involve the precise segmentation of 3D structures in noisy volumetric data, obtained by techniques like magnetic resonance imaging (MRI) or laser scanning microscopy (LSM). Dealing with such data is a challenging task and requires robust and accurate segmentation methods. In this article, we pr...
Article
Full-text available
Traffic sign detection and recognition is an im-portant part of advanced driver assistance systems. Many prototype solutions for this task have been developed, and first commercial systems have just become available. Their image processing chain can be devided into three steps, pre-processing, detection, and recognition. Albeit several reliable sig...
Conference Paper
Full-text available
In this paper we evaluate several regularization schemes applied to the problem of force estimation, that is Traction Force Microscopy (TFM). This method is widely used to investigate cell adhesion and migration processes as well as cellular response to mechanical and chemical stimuli. To estimate force densities TFM requires the solution of an inv...
Article
Full-text available
Mechanosensing is a vital prerequisite for dynamic remodeling of focal adhesions and cytoskeletal structures upon substrate deformation. For example, tissue formation, directed cell orientation or cell differentiation are regulated by such mechanosensing processes. Focal adhesions and the actin cytoskeleton are believed to be involved in these proc...
Article
Full-text available
Migration of cells is one of the most essential prerequisites to form higher organisms and depends on a strongly coordinated sequence of processes. Early migratory events include substrate sensing, adhesion formation, actin bundle assembly and force generation. While substrate sensing was ascribed to filopodia, all other processes were believed to...
Article
In this study, protein-coated giant phospholipid vesicles were used to model cell plasma membranes coated by surface protein layers that increase membrane stiffness under mechanical or osmotic stress. These changed mechanical properties like bending stiffness, membrane area compressibility modulus, and effective Young's modulus were determined by m...
Article
Most cells of the connective tissue have to cope with sizeable mechanical strains which also serve as cues to initiate cell responses like cell shape changes, cell reorientation and rearrangement of the cellular cytoskeleton. In our experiments we cultivated human umbilical cord fibroblasts in elastic chambers and exposed them to cyclic external st...

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Projects (4)
Project
The Lamarr Institute stands for "Triangular AI" – a new, high-performance generation of Artificial Intelligence that is not only trained on data, but also uses additional knowledge and contextual information. In doing so, we direct our research focus on Machine Learning in a way that software and hardware operate in a sustainable and resource-efficient manner.
Project
Methods and Measures to Safeguard AI-based Perception Functions for Automated Driving. The objective of the research project KI Absicherung is to develop an example safeguarding strategy for the use of AI functions in autonomous driving. A key activity is to make the inner workings of these AI functions more transparent. For the first time the project brings together a team of experts on AI algorithms, 3D visualisation, animation, and functional safety.