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Derzeitige Projekte am Institut für Neuroinformatik in Bochum beschäftigen sich mit der Analyse von Straßenverkehrsszenen mittels Computer Vision [12]. Dies impliziert, wegen der durch die natürliche Umwelt aufgestellten Randbedingungen, hohe Anforderungen an die zu entwickelnden Algorithmen. Im speziellen wird versucht, Verkehrsteilnehmer aus Videobildern zu extrahieren und die so gewonnenen Objekthypothesen weiter zu attributieren (z.B. Objektklasse, Abstand, Geschwindigkeit, Gefahrenpotential hinsichtlich der beabsichtigten Eigentrajektorie etc.), um im Hinblick auf den Einsatz in Assistenzsystemen in Fahrzeugen eine möglichst genaue Beschreibung der Umwelt zu erreichen. Nicht nur die große Vielfalt der unterschiedlichen Umweltszenarien, sondern auch das hohe Maß an Sicherheit, das die gestellte Aufgabe erfordert, bedingen ein breitbandiges und flexibles Gesamtsystem [6]. Ein Lösungsvorschlag wird im folgenden behandelt.
Systems for automated image analysis are useful for a variety of tasks and their importance is still growing due to technological advances and an increase of social acceptance. Especially in the field of driver assistance systems the progress in science has reached a level of high performance. Fully or partly autonomously guided vehicles, particularly for road-based traffic, pose high demands on the development of reliable algorithms due to the conditions imposed by natural environments. At the Institut für Neuroinformatik methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We introduce a system which extracts the important information from an image taken by a CCD camera installed at the rear view mirror in a car. The approach consists of a sequential and a parallel sensor and information processing. Three main tasks namely the initial segmentation (object detection), the object tracking and the object classification are realized by integration in the sequential branch and by fusion in the parallel branch. The main gain of this approach is given by the integrative coupling of different algorithms providing partly redundant information.
In this work methods are described, which are used for an individual adaption of a dialog system. Anyway, an automatic real-time capable visual user attention estimation for a face to face human machine interaction is described. Furthermore, an emotion estimation is presented, which combines a visual and an acoustic method. Both, the attention estimation and the visual emotion estimation based on Active Appearance Models (AAMs). Certainly, for the attention estimation Multilayer Perceptrons (MLPs) are used to map the Active Appearance Parameters (AAM-Parameters) onto the current head pose. Afterwards, the chronology of the head poses is classified as attention or inattention. In the visual emotion estimation the AAM-Parameter will be classified by a Support-Vector-Machine (SVM). The acoustic emotion estimation also use a SVM to classifies emotion related audio signal features into the 5 basis emotions (neutral, happy, sad, anger, surprise). Afterward, a Bayes network is used to combine the results of the visual and the acoustic estimation in the decision level. The visual attention estimation as well as the emotion estimation will be used in service robotic to allow a more natural and human like dialog. Furthermore, the human head pose is very efficient interpreted as head nodding or shaking by the use of adaptive statistical moments. Especially, the head movement of many demented people are restricted, so they often only use their eyes to look around. For that reason, this work examine a simple gaze estimation with the help of an ordinary webcam. Moreover, a full body user re-identification method is described, which allows an individual state estimation of several people for hight dynamic situations. In this work an appearance based method is described, which allows a fast people re-identification over a short time span to allow the usage of individual parameter.
CoRA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand-over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot’s gripper (force sensing). The design objective has been to exploit the human operator’s intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
As service robotics research advances rapidly, availability of objective, reproducible test specifications and evaluation criteria and also of benchmarking is more and more felt to be desirable in the community. As a first step towards benchmarking, in this paper we propose a formalization of tests - exemplified for domestic grasp&place tasks. The underlying philosophy of our approach is to confront the robot system in a black-box manner with requirements of a “rational customer”, and characterize the performance of the system in an objective way by the outcomes of a test-suite tailored to this scenario. A formalized single test description consists of a clear and reproducible specification of the robot’s task and the full context on the one hand, and a number of figures which objectively characterize the test result on the other hand. We illustrate this methodology for the domestic assistance scenario.
This article describes the current state of our research on anthropomorphic robots. Our aim is to make the reader familiar with the two basic principles our work is based on: anthropomorphism and dynamics. The principle of anthropomorphism means a restriction to human-like robots which use version, audition and touch as their only sensors so that natural man-machine interaction is possible. The principle of dynamics stands for the mathematical framework based on which our robots generate their behavior. Both principles have their root in the idea that concepts of biological behavior and information processing can be exploited to control technical systems.
While more and more nuclear installations facing the end of their lifetime, decommissioning financing issues gain importance in political discussions.
The financing needs are huge along the Uranium value chain. Following the polluter pays principle the operator of a nuclear installation is expected to accumulate all the necessary decommissioning funds during the operating life of its facility. However, since decommissioning experience is still limited,
since the decommissioning process can take several decades and since the time
period between the shutdown of a nuclear installation and the final disposal of radioactive waste can be very long, there are substantial risks that costs will be underestimated and that the liable party and the funds accumulated might
not be available anymore when decommissioning activities have to be paid.
Nevertheless, these financing risks can be reduced by the implementation of transparent, restricted, well-governed decommissioning financing schemes, with a system of checks and balances that aims at avoiding negative effects
stemming from conflicts of interests.
Nowadays, teachers and students utilize different ICT devices for conducting innovative and educational activities from anywhere at any time. The enactment of these activities relies on robust communication and computational infrastructures used for supporting technological devices enabling better accessibility to educational resources and pedagogical scaffolds, wherever and whenever necessary. In this paper, we present EDU.Tube: an interactive environment that relies on web and mobile solutions offered to teachers and students for authoring and incorporating educational interactions at specific moments along the time line of occasional YouTube video-clips. The teachers and students could later experience these authored artefacts while interacting from their stationary or mobile devices. We describe our efforts related to the design, deployment and evaluation of an educational activity supported by the EDU.Tube environment. Furthermore, we illustrate the specific teachers’ and students’ efforts practiced along the different phases of this educational activity. The evaluation of this activity and results are presented, followed by a discussion of these findings, as well as some recommendations for future research efforts further elaborating on EDU.Tube’s aspects in relation to learning analytics.
We present a study on 3D based hand pose recognition using a new generation of low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. We investigate the performance of different 3D descriptors, as well as the fusion of two ToF sensor streams. By basing a data fusion strategy on the fact that multilayer perceptrons can produce normalized confidences individually for each class, and similarly by designing information-theoretic online measures for assessing confidences of decisions, we show that appropriately chosen fusion strategies can improve overall performance to a very satisfactory level. Real-time capability is retained as the used 3D descriptors, the fusion strategy as well as the online confidence measures are computationally efficient.