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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications III"

共 369 条记录，以下是91-100 订阅

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fusion of Hyperspectral and LADAR Data for Autonomous Target Detection

Fusion of Hyperspectral and LADAR Data for Autonomous Target...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Kanaev, A. V. Walls, T. J. USN Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819486387

Robust fusion of data from disparate sensor modalities can provide improved target detection performance over those attainable with the individual sensors. In particular, detection of low-radiance manmade objects or objects under shadow obscuration in hyperspectral imagery (HSI) with acceptable false alarm rates has proven especially challenging. We have developed a fusion algorithm for the enhanced detection of difficult targets when the HSI data is simultaneously collected with LADAR data. Initial detections are obtained by applying a sub-space RX (SSRX) algorithm to the HSI data. In parallel, LADAR-derived digital elevation map (DEM) is segmented and coordinates of objects within a specific elevation range and size are returned to the HSI processor for their spectral signature extraction. Each extracted signature that has not been already detected by SSRX is used in secondary HSI detection employing the adaptive cosine estimator (ACE) algorithm. We show that spatial distribution of ACE score allows for confident discrimination between background elevations and manmade objects. Key to cross-characterization of the data is the accurate co-alignment of the image data. We have also developed an algorithm for automatic co-registration of ladar and HSI imagery, based on the maximization of mutual information, which can provide accurate, sub-pixel registration even if the case when the imaging geometries for the two sensors differ. Details of both algorithms will be presented and results from application to field data will be discussed.

关键词： data fusion hyperspectral imagery LADAR registration maximization of mutual information

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ULTRASONIC IMAGING OF MATERIAL FLAWS EXPLOITING MULTIPATH INFORMATION

ULTRASONIC IMAGING OF MATERIAL FLAWS EXPLOITING MULTIPATH IN...

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Shen, Xizhong Zhang, Yimin D. Demirli, Ramazan Amin, Moeness G. Villanova Univ Ctr Adv Commun Villanova PA 19085 USA Shanghai Inst Technol Elect & Automat Sch Shanghai Peoples R China

ISBN: (纸本)9780819486387

In this paper, we consider ultrasonic imaging for the visualization of flaws in a material. Ultrasonic imaging is a powerful nondestructive testing (NDT) tool which assesses material conditions via the detection, localization, and classification of flaws inside a structure. Multipath exploitations provide extended virtual array apertures and, in turn, enhance imaging capability beyond the limitation of traditional multisensor approaches. We utilize reflections of ultrasonic signals which occur when encountering different media and interior discontinuities. The waveforms observed at the physical as well as virtual sensors yield additional measurements corresponding to different aspect angles. Exploitation of multipath information addresses unique issues observed in ultrasonic imaging. (1) Utilization of physical and virtual sensors significantly extends the array aperture for image enhancement. (2) Multipath signals extend the angle of view of the narrow beamwidth of the ultrasound transducers, allowing improved visibility and array design flexibility. (3) Ultrasonic signals experience difficulty in penetrating a flaw, thus the aspect angle of the observation is limited unless access to other sides is available. The significant extension of the aperture makes it possible to yield flaw observation from multiple aspect angles. We show that data fusion of physical and virtual sensor data significantly improves the detection and localization performance. The effectiveness of the proposed multipath exploitation approach is demonstrated through experimental studies.

关键词： Ultrasonic imaging information fusion multipath exploitation nondestructive evaluation flaw detection

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Multisensor fusion-Based object detection and tracking using Active Shape Model

Multisensor fusion-Based object detection and tracking using...

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2011 6th International conference on Digital Information Management, ICDIM 2011

作者： Lee, Dongeun Choi, Sunghoon Information Technology Research Center LG CNS Co. Ltd. Seoul Korea Republic of

ISBN: (纸本)9781457715389

This paper proposes automatic target detection and tracking system using Active Shape Model (ASM). Existing model based approaches for tracking are either manually initiated or need some form of user interaction to locate the object in images. Also the low light environmental conditions for surveillance systems make the tracking further harder. Hence the proposed system makes use of multiple sensors in the form of IR and visible cameras to enable tracking in degraded and low light environments. The proposed algorithm consists of the following stages: (i) input image evaluation for obtaining the conditions under which the camera is placed, (ii) an integrated motion detector and target tracker, (iii) active shape tracker(AST) for performing tracking, (iv) update of tracking results for real time tracking of targets. In the first stage the input image is evaluated for the lighting conditions. If the lighting conditions are poor then IR sensor is integrated with the CCD sensor for tracking applications. In the second stage the motion detector and region tracker are used to provide feedback to AST for automatic initialization of tracking. Tracking is carried out in the third stage using ASM. The final stage extracts the parameters and tracking information and applies it to the next frame if the tracking is carried out in real time. The major contribution this work lies in the integration for a completed system, which covers from image processing to tracking algorithms. The approach of combining multiple algorithms succeeds in overcoming fundamental limitations of tracking and at the same time realizes real time implementation. Experimental results show that the proposed algorithm can track people under various environment in real-time. The proposed system has potential uses in the area of surveillance, shape analysis, and model-based coding. © 2011 IEEE.

关键词： Target tracking

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Multisensor fusion-based object detection and tracking using Active Shape Model

Multisensor fusion-based object detection and tracking using...

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International conference on Digital Information Management (ICDIM)

作者： Dongeun Lee Sunghoon Choi Information Technology Research Center LG CNS Company Limited Seoul South Korea

This paper proposes automatic target detection and tracking system using Active Shape Model (ASM). Existing model based approaches for tracking are either manually initiated or need some form of user interaction to locate the object in images. Also the low light environmental conditions for surveillance systems make the tracking further harder. Hence the proposed system makes use of multiple sensors in the form of IR and visible cameras to enable tracking in degraded and low light environments. The proposed algorithm consists of the following stages: (i) input image evaluation for obtaining the conditions under which the camera is placed, (ii) an integrated motion detector and target tracker, (iii) active shape tracker (AST) for performing tracking, (iv) update of tracking results for real time tracking of targets. In the first stage the input image is evaluated for the lighting conditions. If the lighting conditions are poor then IR sensor is integrated with the CCD sensor for tracking applications. In the second stage the motion detector and region tracker are used to provide feedback to AST for automatic initialization of tracking. Tracking is carried out in the third stage using ASM. The final stage extracts the parameters and tracking information and applies it to the next frame if the tracking is carried out in real time. The major contribution this work lies in the integration for a completed system, which covers from image processing to tracking algorithms. The approach of combining multiple algorithms succeeds in overcoming fundamental limitations of tracking and at the same time realizes real time implementation. Experimental results show that the proposed algorithm can track people under various environment in real-time. The proposed system has potential uses in the area of surveillance, shape analysis, and model-based coding.

关键词： Shape Training Laplace equations Real time systems Computer vision Target tracking

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Multisensor Data fusion with Disparate Data Sources

Multisensor Data Fusion with Disparate Data Sources

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Minor, Christian P. Hammond, Mark H. Johnson, Kevin J. Rose-Pehrsson, Susan L. Nova Res Inc 1900 Elkin St 320 Alexandria VA 22308 USA US Navy Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819481740

In many instances, sensing tasks are best addressed with multiple sensing modalities. However, fusion of the outputs of disparate sensor systems presents a significant challenge to forming a cohesive sensing system. A discussion of strategies for fusion of disparate sensor data is presented and illustrated with examples of real time and retrospective data fusion for multisensor systems. The first example discussed is a real-time system for situational awareness and the detection of damage control events in ship compartments. The second example is a retrospective data fusion framework for a multisensor system for the detection of buried unexploded ordnance at former bomb and target ranges.

关键词： data fusion multisensor system fire detection UXO remediation

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Multi-Layered Context Impact Modulation for Enhanced Focus of Attention of Situational Awareness in Persistent Surveillance Systems

Multi-Layered Context Impact Modulation for Enhanced Focus o...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Shirkhodaie, Amir Rababaah, Haroun Tennessee State Univ Dept Mech & Mfg Engn Ctr Excellence Battlefield Sensor Fus Nashville TN 37209 USA

ISBN: (纸本)9780819481740

This paper presents a Multi-Layered Context Impact Modulation (MCIM) technique for persistent surveillance systems (PSS) and discusses its layered architecture for different context modulations including: spatial, temporal, sensor reliability, human presence, and environmental modulations. This paper also presents a fusion model for enhancement of focus of attention at the common operation picture (COP). The fusion model combines all the impacts from the different MCIM layers onto one unified modulated map. To test and evaluate the performance of MCIM, several experiments were conducted to modulate interaction of humans and vehicles which exhibit various normal and suspicious behaviors. The experimental results show strength of this approach in correctly modulating different suspicious situations with higher degree of certainty.

关键词： Persistent Surveillance Systems Situation Assessment Context Impact Layered Modulation Energy Logic fusion Engine Large-scale sensor Networks Soft fusion

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A Copula Based Semi-parametric Approach for Footstep Detection Using Seismic sensor Networks

A Copula Based Semi-parametric Approach for Footstep Detecti...

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Sundaresan, Ashok Subramanian, Arun Varshney, Pramod K. Damarla, Thyagaraju Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Army Res Labs Adelphi MD 20783 USA

ISBN: (纸本)9780819481740

In this paper, we consider the problem of detecting the presence of footsteps using signal measurements from a network of seismic sensors. Since the sensors are closely spaced, they result in correlated measurements. A novel method for detection that exploits the spatial dependence of sensor measurements using copula functions is proposed. An approach for selecting the copula function that is most suited for modeling the spatial dependence of sensor observations is also provided. The performance of the proposed approach is illustrated using real footstep signals collected using an experimental test-bed consisting of seismic sensors.

关键词： sensors Seismic sensors Data modeling sensor networks Statistical modeling Signal detection Performance modeling Analytical research Statistical analysis Surveillance

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Implementing Real-Time Imaging Systems Using the Sarnoff Acadia II Vision Processor

Implementing Real-Time Imaging Systems Using the Sarnoff Aca...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Berends, David van der Wal, Gooitzen Sarnoff Corp Princeton NJ 08543 USA

ISBN: (纸本)9780819481740

Vision system designers often face the daunting challenge of implementing powerful image processing capabilities in severely size, weight and power constrained systems. Multi-sensor fusion, image stabilization, image enhancement, target detection and object tracking are fundamental processing techniques required by UAVs (Unmanned Aerial Vehicles), smart cameras, weapon sights, and vehicle situational awareness systems. All of these systems also process non-vision data while communicating large amounts of information elsewhere. To meet their demanding requirements, Sarnoff developed the Acadia (R) II System-on-a- Chip, combining dedicated image processing cores, four ARM (R) 11 processors and an abundance of peripherals in a single Integrated Circuit. This paper will describe how to best use the power of the Acadia (R) II as both an all-in-one image processor and as a general purpose computer for performing other critical non-vision tasks, such as flight control and system-to-system communication.*

关键词： fusion stabilization image enhancement size weight and power constrained systems target detection object tracking system-on-a-chip situational awareness

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fusion of ESM reports through Dempster-Shafer and Dezert-Smarandache theories

Fusion of ESM reports through Dempster-Shafer and Dezert-Sma...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Valin, Pierre Djiknavorian, Pascal Bosse, Eloi Grenier, Dominic R&D Canada Valcartier Decis Support Syst Sect C2 2459 Blvd Pie XI Nord Quebec City PQ G3J 1X5 Canada Univ Laval Dept Genie Electr & Genie Informat Laval G1V 0A6 PQ Canada

ISBN: (纸本)9780819481740

We address the problem of fusing ESM reports by two evidential reasoning schemes, namely Dempster-Shafer theory and Dezert-Smarandache theory. These schemes provide results in different frames of discernment, but are able to fuse realistic ESM data. We discuss their advantages and disadvantages under varying conditions of sensor data certainty and fusion reliability, the latter coming from errors in the association process. A thresholded version of Dempster-Shafer theory is fine-tuned for performance across a wide range of values for certainty and reliability. The results are presented first for typical scenarios, and secondly for Monte-Carlo studies of scenarios under varying sensor certainty and fusion reliability. The results exhibit complex non-linear functions, but for which clear trends can nevertheless be extracted. A compromise has to be achieved between stability under occasional miss-associations, and reaction time latency under a real change of allegiance. The alternative way of reporting results through Dezert-Smarandache theory is studied under similar conditions, and shown to provide good results, which are however more dependent on the unreliability, and slightly less stable. In this case however, the frame of discernment is larger, and permits additional interpretations, which are outside the scope of Dempster-Shafer.

关键词： Evidential reasoning realistic data Dempster-Shafer Dezert-Smarandache

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Multiple hypothesis tracking of two persons using a network of LIDAR sensors with stationary and directional beams

Multiple hypothesis tracking of two persons using a network ...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Wenzl, Konrad Ruser, Heinrich Kargel, Christian Bundeswehr Univ Munich Inst Measurement & Automat Div Sensor Technol & Measurement Syst D-85577 Neubiberg Munich Germany

ISBN: (纸本)9780819481740

A sensor network based on the LIDAR (LIght Detection And Ranging) principle is investigated in order to track persons inside a surveillance area and be able to identify security-relevant behavior. In order to minimize the overall sensor network complexity, power consumption and costs, we recently investigated the network topology based on a quality measure in terms of the number of nodes, measurement distance, width of the LIDAR beams and localization as well as classification performance. As a result, stationary beams with rather small opening angles of up to a few degrees are a good compromise. Since certain regions of the surveillance area are not directly assessed by the LIDAR beams, the accurate tracking of a target throughout the entire area of surveillance is challenging. We demonstrated that tracking based on a Kalman filter approach can nevertheless deliver satisfactory results for a single person inside the surveillance area. In this paper we focus on the task of reliably tracking two persons inside the surveillance area at the same time. The tracking of multiple moving targets is carried out by applying a Multiple Hypothesis Tracking filter approach. The localization and tracking performances are derived from simulations and experiments carried out with commercial laser scanners. In the near future, the rather expensive laser scanners will be replaced by appropriate LIDAR range finders.

关键词： sensor Network Surveillance Multiple Target Tracking Multiple Hypothesis Tracking Kalman Tracking

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