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International Conference on information Fusion

作者： Kathrin Wilkens Martina Daun Institute of Digital Signal Processing Faculty of Engineering University of Kiel Kiel Germany Department Sensor Data and Information Fusion Fraunhofer FKIE Wachtberg Germany

ISBN: (纸本)9781457702679

In active sonar, low measurement rates of up to several minutes are commonly used. Thus, strong manoeuvres may occur between two measurements. To assure high-quality tracking, target dynamics have to be modelled precisely. In this paper, a variant of the Nearly Constant Velocity model is presented with deviations modelled according to the assumption that for submarine manoeuvres a change in heading is more likely than a change in speed. The proposed motion model uses Gaussian Mixtures to approximate the non-Gaussian distribution after the Kalman Filter prediction step. It is realised in the framework of multi-hypothesis tracking and applied to the Metron data set which simulates sonar contacts detected by 25 distributed sonobuoys. A high amount of low-quality input data is provided to the tracker. Thus, this paper additionally proposes a contact-fusion approach to display the measurements of a single time step and of multiple sensors into Cartesian coordinates.

关键词： Target tracking Noise Approximation methods Uncertainty Sonar Kalman filters

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Benefits from Distributed Control in the PRP Industry

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IFAC Proceedings Volumes 1980年第4期13卷 480-482页

作者： B. Biornstad E. Jutila R. Koreman H.H. Steusloff J. van Reusel Honeywell Europe Brussels Belgium University of Oulu Finland Agfa-Gevaert Mortsel Belgium Fraunhofer Institute for Information and Data Processing Karlsruhe Federal Republic of Germany BP Chemicals Zwijndrecht Belgium

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A Gaussian mixture motion model and contact fusion applied to the metron data set

A Gaussian mixture motion model and contact fusion applied t...

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作者： Wilkens, Kathrin Daun, Martina Institute for Digital Signal Processing Faculty of Engineering University of Kiel Kiel Germany Bundeswehr Technical Centre for Ships and Naval Weapons Naval Technology and Research Research Department for Underwater Acoustics and Marine Geophysics Kiel Germany Fraunhofer FKIE Department Sensor Data and Information Fusion Wachtberg Germany

ISBN: (纸本)9781457702679

关键词： Sonar

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Life cycle management for cooperative groups of cognitive automobiles in a distributed environment

Life cycle management for cooperative groups of cognitive au...

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IEEE Symposium on Intelligent Vehicle

作者： Christian Frese Thomas Batz Martin Wieser Jurgen Beyerer Lehrstuhl für Interaktive Echtzeitsysteme Fakultät für Informatik University of Karlsruhe Karlsruhe Germany Institute for Information and Data Processing Fraunhofer IITB Karlsruhe Germany

Vehicle traffic in cities and on motorways is increasing, resulting in traffic jams and trip durations which can hardly be calculated. A reduction of dangerous situations and a higher level of traffic flow can be achieved only partially if single vehicles are considered. Controlling a cluster of cognitive vehicles has more potential for improvement if they are acting in a similar way, e.g. driving at the same time on a motorway in the same direction or approaching a junction. This cluster is called a cooperative group, and acts with sensors to perceive the environment, actuators to influence the maneuver of the vehicles and car-to- car-communication to exchange relevant information between the vehicles. In this paper we will describe a distributed algorithm for the life cycle management of these cooperative groups and introduce a common relevant picture as basis for situation recognition and decision making in a complex traffic scenario. Another focus of this contribution is the microscopic simulation framework developed within the project. It incorporates an agent based component realizing the life cycle management of cooperative groups.

关键词： Vehicles

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Security assistant system combining millimetre wave radar sensors and chemical sensors

Security assistant system combining millimetre wave radar se...

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Antennas and Propagation Society International Symposium

作者： Sebastian Hantscher Manfred Hägelen Stefan Lang Helmut Essen Monika Wieneke Wolfgang Koch Axel Tessmann Millimetre Wave Radar and High Frequency Sensors Fraunhofer Institute for High Frequency Physics and Radar Techniques Wachtberg Germany Sensor Data and Information Fusion Fraunhofer Institute for Communication Information Processing and Ergonomics FKIE Wachtberg Germany High Frequency Devices and Circuits Fraunhofer Institute for Solid State Physics IAF Freiburg im Breisgau Germany

This contribution describes a security assistant system comprising distributed millimeter wave radar sensors and chemical sensors. The system is deployable for a wide range of security applications, e. g. passenger security checks on airports, surveillance of people crowds in mass transportation systems or in other security-sensitive areas. The system looks for body-worn hazardous chemical substances in two steps: At first, distributed chemical sensors detect traces of chemicals in a tight tunnel in which multiple persons move freely. These measurements are repeated at different locations whereby the person carrying the chemical can be determined. Afterwards, the radar module scans the person to be screened and yields as the result the exact location of the detected explosive on the body. This facilitates a time-saving manual follow-up check by security staff. In this paper, the main focus is on the system set-up and millimeter wave radars for object imaging.

关键词： Chemical sensors Radar imaging Chemicals Sensors Millimeter wave radar Security

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Non-myopic Sensor Path Planning for Emitter Localization with a UAV

Non-myopic Sensor Path Planning for Emitter Localization wit...

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International Conference on information Fusion

作者： Folker Hoffmann Hans Schily Markus Krestel Alexander Charlish Matthew Ritchie Hugh Griffiths Sensor Data & Information Fusion department Information Processing and Ergonomics FKIE Fraunhofer Institute for Communication Wachtberg Germany Schönhofer Sales and Engineering GmbH Siegburg Germany Fraunhofer FKIE Department of Electrical Engineering University College London London United Kingdom

This paper addresses the problem of localizing a stationary RF emitter with a mobile UAV, equipped with a single directional antenna. By rotating around its vertical axis, it determines a bearing towards the emitter. Our interest is in optimally selecting the measurement positions to achieve a fast localization. The majority of such systems described in the literature use greedy planning to select the next measurement position. This work experimentally tests an algorithm that performs a non-myopic planning until the final localization step. The algorithm is based on the policy rollout principle and showed good performance in previous simulative studies. It is adapted to match the needs of a real world setup and evaluated in flight trials. Adaptions include the avoidance of close range measurements to prevent inaccurate measurements at high elevation, and the filtering of poor measurements.

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Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression

arXiv

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arXiv 2022年

作者： Ott, Felix Raichur, Nisha Lakshmana Rügamer, David Feigl, Tobias Neumann, Heiko Bischl, Bernd Mutschler, Christopher The Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany The Statistical Learning and Data Science LMU Munich Munich Germany Munich Germany The Technical University of Dortmund Germany The Institute of Neural Information Processing Ulm University Ulm Germany

Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate an accurate pose of an object when either the environment or the dynamics are known. Absolute pose regression (APR) techniques directly regress the absolute pose from an image input in a known scene using convolutional and spatio-temporal networks. Odometry methods perform relative pose regression (RPR) that predicts the relative pose from a known object dynamic (visual or inertial inputs). The localization task can be improved by retrieving information from both data sources for a cross-modal setup, which is a challenging problem due to contradictory tasks. In this work, we conduct a benchmark to evaluate deep multimodal fusion based on pose graph optimization and attention networks. Auxiliary and Bayesian learning are utilized for the APR task. We show accuracy improvements for the APR-RPR task and for the RPR-RPR task for aerial vehicles and hand-held devices. We conduct experiments on the EuRoC MAV and PennCOSYVIO datasets and record and evaluate a novel industry *** Codes 68T40, 65D19 Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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BioIS study. Comparative study of biometric identification systems

BioIS study. Comparative study of biometric identification s...

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International Carnahan Conference on Security Technology

作者： A. Zwiesele A. Munde C. Busch H. Daum IGD Darmstadt Fraunhofer Institute of Graphical Data Processing Germany BSI Bonn German Information Security Agency Germany BKA Wiesbaden Federal Criminal Investigation Office of Germany Germany

On 1st April 1999, work on the a.m. BioIS Study commenced. This study was initiated by the Federal Criminal Investigation Office of Germany (BKA) in close cooperation with the German information Security Agency (BSI). The study includes a field investigation, in which 11 physiological (static) and behaviour-specific (dynamic) systems, which were available and supported in Germany, were installed and put into operation in a defined scenario. The field investigation was conducted with approximately 40 users representing different age, employment, educational and ethnic groups. The main objectives of the field investigation are as follows: 1.) To gather experience with the biometric systems and to identify any weaknesses that need to be examined in greater depth during the future course of the study. 2.) To obtain statistical information regarding the frequency with which authorised users are rejected by the various systems. 3.) To observe the behaviour of the users over a prolonged period of time, in order to establish whether or not any changes can be observed. 4.) Dupability: The aim of this part is to analyse and assess the effort that is necessary to dupe biometric systems. 5.) Influence of the various programmable system parameters: This part attempts to investigate the repercussions of the various system setups for the identification attributes. 6.) Influence of the various environmental factors on the identification reliability of the systems.

关键词： Biometrics information security data processing Employment Face recognition Handwriting recognition Fingerprint recognition Frequency information analysis Environmental factors

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An integrated simulation framework for cognitive automobiles

An integrated simulation framework for cognitive automobiles

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IEEE Symposium on Intelligent Vehicle

作者： Stefan Vacek Robert Nagel Thomas Batz Frank Moosmann Rudiger Dillmann Insitute for Computer Science and Engineering University of Karlsruhe Karlsruhe Germany Institute of Communication Networks Munich University of Technology München Germany Fraunhofer IITB Institute for Information and Data Processing Karlsruhe Germany Institute of Measurement and Control University of Karlsruhe Karlsruhe Germany

A cognitive automobile is a complex system. It is an indisputable fact that simulations are valuable tools for the development and testing of such complex systems. This paper presents an integrated closed-loop simulation framework which supports the development of a cognitive automobile. The framework aims at simulating complex traffic scenes in inner-city environments. The key features of the simulation are the generation of synthetic data for high level inference mechanisms, providing data for the analysis of car-to-car communication strategies and evaluation of cooperative vehicle behavior.

关键词： Automobiles Traffic control Communication system control Layout Analytical models Vehicle detection Intelligent vehicles System testing Decision making Path planning

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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