Contemporary concerns in Geographical/Geospatial Information Systems (GIS) processing

Blasch, Erik; Pellechia, Matthew F.; Deignan, Paul B.; Palaniappan, Kannappan; Dockstader, S.L.; Seetharaman, Gunasekaran

doi:10.1109/naecon.2011.6183099

Cited by 35 publications

(17 citation statements)

References 31 publications

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“…One recent complex challenge that requires contextual information is WAMI target tracking [107] with information management that supports moving intelligence [108]. Complications of real-time WAMI sensor processing include a low frame rates [109] and mappings to geospatial intelligence systems [110] such as environmental (e.g., terrain modeling). Together, machine analytics and contextual tracking support enhanced situation awareness [111] for cooperative control of multimodal sensors as depicted in Fig.…”

Section: Contextual Tracking Sensors Targets and Environmentsmentioning

confidence: 99%

Contextual Tracking Approaches in Information Fusion

Blasch

Yang²,

García

et al. 2016

Context-Enhanced Information Fusion

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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 chapter, 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 designs to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application

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Section: Contextual Tracking Sensors Targets and Environmentsmentioning

confidence: 99%

Contextual Tracking Approaches in Information Fusion

Blasch

Yang²,

García

et al. 2016

Context-Enhanced Information Fusion

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“…However, Wide Area Surveillance (WAS): the environment is continuously monitored by a sensing system One source of data is the wide-area large format (WALF) video that is airborne imagery characterized by large spatial coverage, high resolution of about 25 cm GSD (Ground Sampling Distance) and low frame rate of a few frames per second. Wide-area large format imagery is also known by several other terms including wide-area aerial surveillance (WAAS), wide-area persistent surveillance (WAPS), Large Volume Streaming Data (LVSD) and wide-area motion imagery (WAMI) [1,4,6,7].…”

Section: Motivationmentioning

confidence: 99%

“…1 The form of f [·] is not known and is to be approximated empirically. A training set D of n measurements has been collected, where D = {(x i , y i )|i = 1, 2, .…”

Section: Approximating Functions Using Gaussian Processesmentioning

confidence: 99%

Modelling and Characterisation of Detection Models in WAMI for Handling Negative Information

Julier¹,

Gning²,

Teacy³

et al. 2014

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information SPONSOR/MONITOR'S ACRONYM(S)AFRL/AFOSR/IOE (EOARD) SPONSOR/MONITOR'S REPORT NUMBER(S) AFRL-AFOSR-UK-TR-2014-000612. DISTRIBUTION/AVAILABILITY STATEMENT Distribution A: Approved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTThe objective of the project reported here was to develop, implement and evaluate a model of the probability of detection of moving objects in Wide Area Motion Imagery (WAMI) that incorporates the effects of the target, the platform, and the environment. Developing situation awareness is vital for almost any kind of military operation. Through understanding the state and nature of the environment, military personnel can plan and respond accordingly. Situation awareness is often treated as the problem of knowing where all the potential targets are. Through knowing the locations of these targets, threats can be identified and countered. Another important source of awareness is to understand where targets cannot be. Regions that are free of targets can be used to constrain where targets might be. To meet these needs, Wide Area Surveillance (WAS) systems have been developed that are able to sense large swaths of an environment simultaneously and at high resolution. However, the next key challenge is to automatically analyze this image data to, for example, track the locations of targets and identify potential anomalous behavior. This report begins to explore how the output from a WAS system can be used by a state-of-the-art multi-target tracker. In particular, we considered how the output of the image processing and matching algorithms used in the Likelihood of Features Tracker (LoFT) could be combined with a Probabilistic Hypothesis Density (PHD) Filter. Using machine learning techniques, we have developed a formalism and algorithms to automatically predict how the visual appearance of a vehicle can change over time. Using this prediction model, we are then able to automatically threshold and detect potential candidate vehicle locations, and assess both probability of detection and the probability of clutter. SUBJECT TERMS Executive SummaryDeveloping situation awareness is vital for almost any kind of military operation. Through understanding the state and nature of the environment, military personnel can plan and respond accordingly. Situation awareness is often treated as the problem of knowing where all the potential targets are. Through knowing the locations of these targets, threats can be identified and countered. To meet these needs, Wide Area Su...

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“…Complications of real-time WAMI sensor processing include a low frame rates [105] and mappings to geospatial intelligence systems [106] such as environmental (e.g., terrain modeling). Together, machine analytics and contextual tracking support enhanced situation awareness [107] for cooperative control of multimodal sensors as depicted in Fig.…”

Section: Contextual Tracking Examplementioning

confidence: 99%

Overview of contextual tracking approaches in information fusion

et al. 2013

Self Cite

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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.

show abstract

Contemporary concerns in Geographical/Geospatial Information Systems (GIS) processing

Cited by 35 publications

References 31 publications

Contextual Tracking Approaches in Information Fusion

Contextual Tracking Approaches in Information Fusion

Modelling and Characterisation of Detection Models in WAMI for Handling Negative Information

Overview of contextual tracking approaches in information fusion

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