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检索条件"任意字段=Sensor Fusion: Architectures, Algorithms and Applications II"

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A preprocessing and automated algorithm selection system for image registration

A preprocessing and automated algorithm selection system for...

引用

Conference on Multisensor, Multisource Information fusion

作者： Drozd, A. L. Blackburn, A. C. Kasperovich, I. P. Varshney, P. K. Xu, M. Kumar, B. Syracuse Univ Syracuse NY 13244 USA

ISBN: (纸本)0819462985

Image registration is a technique for precisely aligning the content of two or more images. It is often used as a preprocessing stage for further analysis, such as automatic target recognition, change detection, and environmental remote sensing. However, there are many different registration algorithms available to the image analyst, and it's difficult to know which one is the best one to use for a particular pair of images. These various algorithms also have a multitude of settings and parameters that must be given proper values for best results. Consequently, it is often difficult to know which algorithm will perform the best in a given situation, under constraints of time or accuracy. We propose constructing an expert system, with rules based on experimental results, that will automatically select the appropriate registration algorithm and perform appropriate preprocessing steps to prepare the images for registration.

关键词： image registration data fusion image processing expert systems

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Investigation of target tracking performance for unattended ground sensor networks employing Distributed Cluster Management

Investigation of target tracking performance for unattended ...

引用

Conference on Multisensor, Multisource Information fusion

作者： Stelzig, Chad A. Essawy, Magdi A. Minor, Sharon Gen Dynam Adv Informat Syst 8800 Queen Ave S Bloomington MN 55431 USA US Army CECOM Ctr Night Vis & Electroopt Ft Belvoir VA 22060 USA

ISBN: (纸本)0819462985

Interest in the distribution of processing in unattended ground sensing (UGS) networks has resulted in new technologies and system designs targeted at reduction of communication bandwidth and resource consumption through managed sensor interactions. A successful management algorithm should not only address the conservation of resources, but also attempt to optimize the information gained through each sensor interaction so as to not significantly deteriorate target tracking performance. This paper investigates the effects of Distributed Cluster Management (DCM) on tracking performance when operating in a deployed UGS cluster. Originally designed to reduce communications bandwidth and allow for sensor field scalability, the DCM has also been shown to simplify the target tracking problem through reduction of redundant information. It is this redundant information that in some circumstances results in secondary false tracks due to multiple intersections and increased uncertainty during track initiation periods. A combination of field test data playback and Monte Carlo simulations are used to analyze and compare the performance of a distributed UGS cluster to that of an unmanaged centralized cluster.

关键词： unattended ground sensors Distributed Cluster Management target tracking

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Data fusion cm a distributed heterogeneous sensor network

Data fusion cm a distributed heterogeneous sensor network

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2006

作者： Lamborn, Peter Williams, Pamela J. Mississippi State University 302 B Park Circle Place Starkville MS 39759 United States Sandia National Laboratories MS9159 P.O. Box 969 Livermore CA 94551-0969 United States

ISBN: (纸本)0819462985

Alarm-based sensor systems are being explored as a tool to expand perimeter security for facilities and force protection. However, the collection of increased sensor data has resulted in an insufficient solution that includes faulty data points. Data analysis is needed to reduce nuisance and false alarms, which will improve officials' decision making and confidence levels in the system's alarms. Moreover, operational costs can be allayed and losses mitigated if authorities are alerted only when a real threat is detected. In the current system, heuristics such as persistence of alarm and type of sensor that detected an event are used to guide officials' responses. We hypothesize that fusing data from heterogeneous sensors in the sensor field can provide more complete situational awareness than looking at individual sensor data. We propose a two stage approach to reduce false alarms. First, we use self organizing maps to cluster sensors based on global positioning coordinates and then train classifiers on the within cluster data to obtain a local view of the event. Next, we train a classifier on the local results to compute a global solution. We investigate the use of machine learning techniques, such as k-nearest neighbor, neural networks, and support vector machines to improve alarm accuracy. On simulated sensor data, the proposed approach identifies false alarms with greater accuracy than a weighted voting algorithm.

关键词： sensor data fusion

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Pedestrian detection by Multi-spectral fusion

Pedestrian detection by Multi-spectral fusion

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Conference on Multisensor, Multisource Information fusion

作者： Ma, Yunqian Wang, Zheng Bazakos, Mike Honeywell Labs 3660 Technol Dr Minneapolis MN 55418 USA Honeywell Technol Solut Lab Shanghai 201203 Peoples R China

ISBN: (纸本)0819462985

Security systems increasingly rely on the use of Automated Video Surveillance (AVS) technology. In particular the use of digital video renders itself to internet and local communications, remote monitoring, and to computer processing. AVS systems can perform many tedious and repetitive tasks currently performed by trained security personnel. AVS technology has already made some significant steps towards automating some basic security functions such as: motion detection, object tracking and event-based video recording. However, there are still many problems associated with just these automated functions, which need to be addressed further. Some examples of these problems are: the high "false alarm rate" and the "loss of track" under total or partial occlusion, when used under a wide range of operational parameters (day, night, sunshine, cloudy, foggy, range, viewing angle, clutter, etc.). Current surveillance systems work well only under a narrow range of operational parameters. Therefore, they need be hardened against a wide range of operational conditions. In this paper, we present a Multi-spectral fusion approach to perform accurate pedestrian segmentation under varying operational parameters. Our fusion method combines the "best" detection results from the visible images and the "best" from the thermal images. Commonly, the motion detection results in the visible images are easily affected by noise and shadows. The objects in the thermal image are relatively stable, but they may be missing some parts of the objects, because they thermally blend with the background. Our method makes use of the "best" object components and de-emphasize the "not best".

关键词： pedestrian detection sensor fusion surveillance level set

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Data fusion in multi sensor platforms for wide-area perception

Data fusion in multi sensor platforms for wide-area percepti...

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IEEE Symposium on Intelligent Vehicle

作者： A. Polychronopoulos N. Floudas A. Amditis D. Bank B. van den Broek Institute of Communications and Computer Systems Athens Greece Daimler Chrysler Aerospace Ulm Germany TNO Defensie en Veiligheid Den Haag Netherlands

There is a strong belief that the improvement of preventive safety applications and the extension of their operative range are achieved by the deployment of multiple sensors with wide fields of view (FOV). The paper contributes to the solution of the problem and introduces distributed sensor data fusion architectures and algorithms for an efficient deployment of multiple sensors that give redundant or complementary information for the moving objects. The proposed fusion architecture is based on a modular approach allowing exchangeability and benchmarking using the output of individual trackers, whereas the fusion algorithm gives a solution to the track management problem and the coverage of wide perception areas. The test case is LATERAL SAFE sensor configuration, which monitors the rear and lateral areas of the vehicle. Results show that with the given approach the system is able to maintain the ID of all objects in transition (an object enters a sensor's FOV) and blind areas (no sensor coverage)

关键词： sensor fusion sensor systems Laser radar Infrared sensors Vehicle safety Accidents sensor systems and applications Radar tracking Intelligent sensors Benchmark testing

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Development of a MATLAB Based Target Tracking Simulation Environment

Development of a MATLAB Based Target Tracking Simulation Env...

引用

IEEE Signal Processing and Communications applications (SIU)

作者： Aslan Saranli Baykal İLTAREN TÜBİTAK-UEKAE Ankara Turkey TÜBİTAK-UEKAE İLTAREN Ankara Elektrik ve Elektronik Müihendisliği Bölümü O.D.T.Ü Ankara Turkey Elektrik ve Elektronik Mühendisliği Bölümü O.D.T.Ü Ankara

A simulation environment for tracking of maneuvering targets in clutter is developed in MATLAB. The simulation environment allows to generate 2-dimensional surveillance radar measurements and to run various target tracking algorithms on these measurements. As a first simulation example, IMM-NNJPDA algorithm, which incorporates NNJPDA data association and IMM filter structure, is implemented and the performance of this algorithm is investigated in an example scenario. By this simulator, in the future, it is aimed that statistical test and evaluation of different radar sensors, scenarios, target tracking methods and data fusion architectures will be performed

关键词： MATLAB Target tracking Radar tracking Clutter Surveillance Radar measurements Filters Testing sensor fusion Performance evaluation

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PERFORMANCE AND LIMITATIONS OF DISTRIBUTED INFERENCE AND INFORMATION fusion IN WIRELESS sensor NETWORK applications

PERFORMANCE AND LIMITATIONS OF DISTRIBUTED INFERENCE AND INF...

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作者： Sherry, Richard Ray PennState University Libraries

学位级别：Doctor of Philosophy

Recent advances in sensor design and miniaturization have provided the opportunity for the creation of large distributed wireless sensor networks. There has been significant progress in combining sensing, processing, data storage and communications capabilities, in network self organization, and in optimizing communication architectures. In contrast to most other network applications, wireless sensor networks face a number of special challenges and constraints resulting from 1) lack of hardwired connections (no external power sources, low communications bandwidths, higher communication error rates), 2) small physical size (small onboard energy supply, small antennas/acoustic transducers, small low energy sensors) and 3) elevated sensor node failure rates. One of the key remaining challenges is in the area of inference and information fusion (aggregating/filtering/interpreting the sensor data into useful high level knowledge). Many authors have advocated the use of local distributed inference and fusion algorithms such as the iterative message-passing belief propagation algorithms employed on probabilistic graphical models (Bayesian Networks and Markov Random Fields). However, little research has been performed to assess the performance of these algorithms under the special constraints imposed by wireless sensor network applications. This dissertation reports on a study investigating issues associated with application of these algorithms to realistic wireless sensor networks configurations. This research has produced results delineating the performance and limitations including communications requirements, energy resource requirements and the impacts of different topologies and architectures such as hierarchical/non-hierarchical topologies, centralized or distributed processing, localized (in local node clusters) or full network models, and node cluster size.

关键词： wireless sensor networks information fusion data fusion

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Performance and limitations of distributed inference and information fusion in wireless sensor network applications

Performance and limitations of distributed inference and inf...

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作者： Sherry, Richard R. The Pennsylvania State University

学位级别：Ph.D.

Recent advances in sensor design and miniaturization have provided the opportunity for the creation of large distributed wireless sensor networks. There has been significant progress in combining sensing, processing, data storage and communications capabilities, in network self organization, and in optimizing communication architectures. In contrast to most other network applications, wireless sensor networks face a number of special challenges and constraints resulting from (1) lack of hardwired connections (no external power sources, low communications bandwidths, higher communication error rates), (2) small physical size (small onboard energy supply, small antennas/acoustic transducers, small low energy sensors) and (3) elevated sensor node failure rates. One of the key remaining challenges is in the area of inference and information fusion (aggregating/filtering/interpreting the sensor data into useful high level knowledge). Many authors have advocated the use of local distributed inference and fusion algorithms such as the iterative message-passing belief propagation algorithms employed on probabilistic graphical models (Bayesian Networks and Markov Random Fields). However, little research has been performed to assess the performance of these algorithms under the special constraints imposed by wireless sensor network applications. This dissertation reports on a study investigating issues associated with application of these algorithms to realistic wireless sensor networks configurations. This research has produced results delineating the performance and limitations including communications requirements, energy resource requirements and the impacts of different topologies and architectures such as hierarchical/non-hierarchical topologies, centralized or distributed processing, localized (in local node clusters) or full network models, and node cluster size.

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Proceedings of SPIE: Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2005

Proceedings of SPIE: Multisensor, Multisource Information Fu...

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Multisensor, Multisource Information fusion: Architecture, algorithms, and applications 2005

The proceedings contain 35 papers from the conference on Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2005. Topics discussed include: reduced data projection slice image fusion using principal component analysis;real-time implementation of image alignment and fusion;image fusion with the hybrid evolutionary algorithm and response analysis;a perceptual quality metric for image fusion based on regional information;multispectral sensor fusion using the projection-slice theorem;a situation awareness model applied to multiple domains;hybrid framework for fusion 2+ in multi-multi air engagement;and product-based multiresolution image fusion.

关键词： sensor data fusion

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Scene recognition with audio-visual sensor fusion

Scene recognition with audio-visual sensor fusion

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Conference on Multisensor, Multisource Information fusion

作者： Devicharan, D Mehrotra, KG Mohan, CK Varshney, PK Zuo, L Syracuse Univ Dept EECS Syracuse NY 13244 USA

ISBN: (纸本)0819457981

Several surveillance applications are characterized by the ability to gather information about the scene from more than one sensor modality, and heterogeneous sensor data must then be fused by the decision-maker. hi this paper, we discuss the issues relevant to developing a model for fusion of information from audio and visual sensors, and present a framework to enhance decision-making capabilities. In particular, our methodology focuses on the issues of temporal reasoning, uncertainty representations, and coupling between features inferred from data streams coming from different sensors. We propose a conditional probability-based representation for uncertainty, along with fuzzy rules to assist decision-making, and a matrix representation of the coupling between sensor data streams. We also develop a fusion algorithm that utilizes these representations.

关键词： multimodal sensor fusion scene recognition activity detection audio and visual surveillance

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