4,115 research outputs found
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Architectures for embedded multimodal sensor data fusion systems in the robotics : and airport traffic suveillance ; domain
Smaller autonomous robots and embedded sensor data fusion systems often suffer from limited
computational and hardware resources. Many ‘Real Time’ algorithms for multi modal sensor data
fusion cannot be executed on such systems, at least not in real time and sometimes not at all, because
of the computational and energy resources needed, resulting from the architecture of the
computational hardware used in these systems. Alternative hardware architectures for generic
tracking algorithms could provide a solution to overcome some of these limitations. For tracking and
self localization sequential Bayesian filters, in particular particle filters, have been shown to be able to
handle a range of tracking problems that could not be solved with other algorithms. But particle filters
have some serious disadvantages when executed on serial computational architectures used in most
systems. The potential increase in performance for particle filters is huge as many of the computational
steps can be done concurrently. A generic hardware solution for particle filters can relieve the central
processing unit from the computational load associated with the tracking task.
The general topic of this research are hardware-software architectures for multi modal sensor data
fusion in embedded systems in particular tracking, with the goal to develop a high performance
computational architecture for embedded applications in robotics and airport traffic surveillance
domain. The primary concern of the research is therefore: The integration of domain specific concept
support into hardware architectures for low level multi modal sensor data fusion, in particular
embedded systems for tracking with Bayesian filters; and a distributed hardware-software tracking
systems for airport traffic surveillance and control systems.
Runway Incursions are occurrences at an aerodrome involving the incorrect presence of an aircraft,
vehicle, or person on the protected area of a surface designated for the landing and take-off of aircraft.
The growing traffic volume kept runway incursions on the NTSB’s ‘Most Wanted’ list for safety
improvements for over a decade. Recent incidents show that problem is still existent. Technological
responses that have been deployed in significant numbers are ASDE-X and A-SMGCS. Although these
technical responses are a significant improvement and reduce the frequency of runway incursions,
some runway incursion scenarios are not optimally covered by these systems, detection of runway
incursion events is not as fast as desired, and they are too expensive for all but the biggest airports.
Local, short range sensors could be a solution to provide the necessary affordable surveillance accuracy
for runway incursion prevention. In this context the following objectives shall be reached. 1) Show the
feasibility of runway incursion prevention systems based on localized surveillance. 2) Develop a design
for a local runway incursion alerting system. 3) Realize a prototype of the system design using the
developed tracking hardware.Kleinere autonome Roboter und eingebettete Sensordatenfusionssysteme haben oft mit stark
begrenzter Rechenkapazität und eingeschränkten Hardwareressourcen zu kämpfen. Viele
Echtzeitalgorithmen für die Fusion von multimodalen Sensordaten können, bedingt durch den hohen
Bedarf an Rechenkapazität und Energie, auf solchen Systemen überhaupt nicht ausgeführt werden,
oder zu mindesten nicht in Echtzeit. Der hohe Bedarf an Energie und Rechenkapazität hat seine
Ursache darin, dass die Architektur der ausführenden Hardware und der ausgeführte Algorithmus
nicht aufeinander abgestimmt sind. Dies betrifft auch Algorithmen zu Spurverfolgung. Mit Hilfe von
alternativen Hardwarearchitekturen für die generische Ausführung solcher Algorithmen könnten sich
einige der typischerweise vorliegenden Einschränkungen überwinden lassen. Eine Reihe von Aufgaben,
die sich mit anderen Spurverfolgungsalgorithmen nicht lösen lassen, lassen sich mit dem Teilchenfilter,
einem Algorithmus aus der Familie der Bayesschen Filter lösen. Bei der Ausführung auf traditionellen
Architekturen haben Teilchenfilter gegenüber anderen Algorithmen einen signifikanten Nachteil,
allerdings ist hier ein großer Leistungszuwachs durch die nebenläufige Ausführung vieler
Rechenschritte möglich. Eine generische Hardwarearchitektur für Teilchenfilter könnte deshalb die
oben genannten Systeme stark entlasten.
Das allgemeine Thema dieses Forschungsvorhabens sind Hardware-Software-Architekturen für die
multimodale Sensordatenfusion auf eingebetteten Systemen - speziell für Aufgaben der
Spurverfolgung, mit dem Ziel eine leistungsfähige Architektur für die Berechnung entsprechender
Algorithmen auf eingebetteten Systemen zu entwickeln, die für Anwendungen in der Robotik und
Verkehrsüberwachung auf Flughäfen geeignet ist. Das Augenmerk des Forschungsvorhabens liegt
dabei auf der Integration von vom Einsatzgebiet abhängigen Konzepten in die Architektur von
Systemen zur Spurverfolgung mit Bayeschen Filtern, sowie auf verteilten Hardware-Software
Spurverfolgungssystemen zur Überwachung und Führung des Rollverkehrs auf Flughäfen.
Eine „Runway Incursion“ (RI) ist ein Vorfall auf einem Flugplatz, bei dem ein Fahrzeug oder eine Person
sich unerlaubt in einem Abschnitt der Start- bzw. Landebahn befindet, der einem Verkehrsteilnehmer
zur Benutzung zugewiesen wurde. Der wachsende Flugverkehr hat dafür gesorgt, das RIs seit über
einem Jahrzehnt auf der „Most Wanted“-Liste des NTSB für Verbesserungen der Sicherheit stehen.
Jüngere Vorfälle zeigen, dass das Problem noch nicht behoben ist. Technologische Maßnahmen die in
nennenswerter Zahl eingesetzt wurden sind das ASDE-X und das A-SMGCS. Obwohl diese Maßnahmen
eine deutliche Verbesserung darstellen und die Zahl der RIs deutlich reduzieren, gibt es einige RISituationen
die von diesen Systemen nicht optimal abgedeckt werden. Außerdem detektieren sie RIs
ist nicht so schnell wie erwünscht und sind - außer für die größten Flughäfen - zu teuer. Lokale Sensoren
mit kurzer Reichweite könnten eine Lösung sein um die für die zuverlässige Erkennung von RIs
notwendige Präzision bei der Überwachung des Rollverkehrs zu erreichen. Vor diesem Hintergrund
sollen die folgenden Ziele erreicht werden. 1) Die Machbarkeit eines Runway Incursion
Vermeidungssystems, das auf lokalen Sensoren basiert, zeigen. 2) Einen umsetzbaren Entwurf für ein
solches System entwickeln. 3) Einen Prototypen des Systems realisieren, das die oben gennannte
Hardware zur Spurverfolgung einsetzt
Dynamic Reconfiguration in Camera Networks: A Short Survey
There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration
Overview of contextual tracking approaches in information fusion
Proceedings of: Geospatial InfoFusion III. 2-3 May 2013 Baltimore, Maryland, United States.Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.Publicad
Acceleration of MCMC-based algorithms using reconfigurable logic
Monte Carlo (MC) methods such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) have emerged as popular tools to sample from high dimensional probability distributions. Because these algorithms can draw samples effectively from arbitrary distributions in Bayesian inference
problems, they have been widely used in a range of statistical applications. However, they are often too time consuming due to the prohibitive costly likelihood evaluations, thus they cannot be practically applied to complex models with large-scale datasets. Currently, the lack of sufficiently fast MCMC methods limits their applicability in many modern applications such as genetics and machine
learning, and this situation is bound to get worse given the increasing adoption of big data in many fields. The objective of this dissertation is to develop, design and build efficient hardware architectures for MCMC-based algorithms on Field Programmable Gate Arrays (FPGAs), and thereby bring them closer to practical applications. The contributions of this work include: 1) Novel parallel FPGA architectures of the state-of-the-art resampling algorithms for SMC methods. The proposed architectures allow for parallel implementations and thus improve the processing speed. 2) A novel mixed precision MCMC algorithm, along with a tailored FPGA architecture. The proposed design allows for more parallelism and achieves low latency for a given set of hardware resources, while still guaranteeing unbiased estimates. 3) A new
variant of subsampling MCMC method based on unequal probability sampling, along with a highly optimized FPGA architecture. The proposed method significantly reduces off-chip memory access and achieves high accuracy in estimates for a given time budget. This work has resulted in the development of hardware accelerators of MCMC and SMC for very large-scale Bayesian tasks by applying
the above techniques. Notable speed improvements compared to the respective state-of-the-art CPU and GPU implementations have been achieved in this work.Open Acces
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