Decker, Eric Leadbetter, Ira S. Fosbury, John L. Aved and Erik P. Gomez-Romero, Jesus Garcia, M.
Patricio, M. Serrano, and J.
Patricio, M. Serrano, and J. Du kanske gillar. Permanent Record Edward Snowden Inbunden. Spara som favorit. Skickas inom vardagar. The second example briefly examines the role of context in cyber security. Finally, our fourth example discusses the role of context in a supervisory control problem when managing multiple autonomous systems.
Livingston, Stephen Russell, Jonathan W. Decker, Eric Leadbetter, Ira S.
We propose in this paper, a methodology for combining contextual information and sensors derived information to improve the process of data fusion. Even if this methodology can be used in different types of fusion processes detection, classification, etc. The objective of this combination is to supervise the classical fusion process by context analysis so that this fusion process can adapt itself to the variations of the context.
Target tracking is the estimation of the state of one or multiple, usually moving, objects targets based on a time series of measurements. Widely addressed within the Bayesian statistical framework, it requires the modeling of the target state evolution and the measurement process. Information on the constraints posed by the context in which the target evolves and the measurement geometry is often available. This chapter presents several approaches to exploit different types of context knowledge and demonstrates context-enhanced tracking based on real and simulated data.
Numerical results are given for the inclusion of sea-lanes in ship tracking and route propagation, and for road-map assisted air-to-ground radar tracking. In this chapter, contextual information is discussed for improving tracking of surface vehicles. Contextual information generally involves any kind of information that is not related directly to kinematic sensor measurements.
This information, termed trafficability, is used to incorporate constraints on the vehicle that ultimately deflect the tracks to areas that provide the highest trafficable regions. For example, local terrain slope, ground vegetation and other factors that put constraints on the vehicles can be considered as contextual information.
Both kinematic sensor data and contextual information are tied into the overall tracker design through the use of trafficability maps.
Two specific design examples are summarized in this chapter. The first example involves ground tracking of vehicles where the contextual information exploits terrain information to aid in the tracking. The second example involves a sea-based maritime application where the contextual information exploits depth, marked shipping channel locations, and high-value unit information as contextual information.
Both examples show that the use contextual information can significantly improve tracking performance. Soft information fusion, fusing information from natural language messages with other soft information and with information from physical sensors is facilitated by representing the information in the messages as a formally defined propositional graph that abides by the uniqueness principle—the principle that every entity or event that is mentioned in the message is represented by a unique node in the graph, or, at worst, by several nodes connected by co-referentiality relations.
To further facilitate information fusion, information from the message is enhanced with relevant information from background knowledge sources.
What knowledge is relevant is determined by also representing the background knowledge as a propositional graph, embedding the knowledge graph from the messages into the background knowledge graph using the uniqueness principle to fuse a message graph node with a background knowledge graph node, and then using spreading activation to find subgraphs of the background knowledge graph. Sensor measurements of the state of a system are affected by natural and man-made operating conditions that are not accounted for in the definition of system states.
It is postulated that these conditions, called contexts, are such that the measurements from individual sensors are independent conditioned on each pair of system state and context.
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This postulation leads to kernel-based unsupervised learning of a measurement model that defines a common context set for all different sensor modalities and automatically takes into account known and unknown contextual effects. The resulting measurement model is used to develop a context-aware sensor fusion technique for multi-modal sensor teams performing state estimation. Moreover, a symbolic compression technique, which replaces raw measurement data with their low-dimensional features in real time, makes the proposed context learning approach scalable to large amounts of data from heterogeneous sensors.
The developed approach is tested with field experiments for multi-modal unattended ground sensors performing human walking style classification. The understanding and principled exploitation of contextual information in fusion systems is still very limited. Domain knowledge is generally acquired from an expert and applied to stove-piped solutions that can hardly scale or adapt to new conditions. In this work, a few important elements that should be considered in designing a context-aware system are discussed including: context refinement using terrain information, context to promote fusion results to higher levels of abstraction, and context for resource management.
We highlight concepts of context sifting, shifting, and adaptation for multi-level fusion. Our contention here is that a common representation and description framework is the premise for enabling processing overarching different semantic levels.
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We here expand on our previous works [ 1 , 2 ] further detailing and exemplifying the use of BML and clarifying aspects related to the use of contextual information and the exploitation of uncertain soft input along with sensor readings. Information fusion consists of organizing a set of data for correlation in time, association over collections, and estimation in space.
Context-Enhanced Information Fusion: Boosting Real-World
There exist many methods for object tracking and classification; however, video analytics systems suffer from robust methods that perform well in all operating conditions i. In this chapter we propose a novel framework to fuse video data with text data for enhanced simultaneous tracking and identification. The need for such methodology resides in answering user queries, linking information over different collections, and providing meaningful product reports.
Together, physics-derived and human-derived fusion PHF enhances situation awareness, provides situation understanding, and affords situation assessment. PHF is an example of hard e. A demonstrated example for multimodal text and video sensing is shown where context provides the means for associating the multimode data aligned in space and time. Erik Blasch, Riad I.go
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Context understanding is established from the content, analysis, and guidance from query-based coordination between users and machines. In this chapter, a live video computing LVC structure is presented for access of a database management of information for context assessment. Context assessment includes multimedia fusion of query-based text, images, and exploited tracks which can be utilized for content-based image retrieval CBIR.
In this chapter, we explore the developments in database systems to enable context to be utilized in user-based queries e. Using a common video dataset, we demonstrate time savings in the analysis from user queries to provide a context, privacy, and semantic-aware information fusion. The informational basis for making appropriate decisions is provided by situation pictures that electronically represent a dynamically evolving real-world scenario.
For the rapidly growing area of civil security applications, exploitation of observational information, sensor data as well as textual reports, critically depends on the availability and the quality of appropriate context information as well as on the underlying data fusion algorithms that take them into account.