Video image registration evaluation for a layered sensing environment

Mendoza-Schrock, Olga; Patrick, James A.; Blasch, Erik

doi:10.1109/naecon.2009.5426624

Cited by 61 publications

(28 citation statements)

References 17 publications

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“…Together, contextual environmental modeling of the weather for the aerial sensors and the terrain information for the ground sensors would aid in the analysis of the complete system for accurate automatic target recognition (ATR) (i.e., high probability of detection with low false alarms [102]). One recent complex challenge that requires contextual information is WAMI target tracking [103] with information management that supports moving intelligence [104].…”

Section: Contextual Tracking Examplementioning

confidence: 99%

Overview of contextual tracking approaches in information fusion

et al. 2013

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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.

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Section: Contextual Tracking Examplementioning

confidence: 99%

Overview of contextual tracking approaches in information fusion

et al. 2013

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“…Since the WAMI data covers a city (Figure 3), the ability to maintain track (after initiation) is increased as the object is within the sensed region of interest for potentially an extended duration. [2]. Likewise, with constant staring, there is the increased advantage of extending track lifetime by linking associated tracks, projecting tracks onto road networks [35], recovering from lost tracks, and coordinating handoff to ground sensors.…”

Section: Wide Area Motion Imagery (Sensors)mentioning

confidence: 99%

“…Using the organized data sets for dismount tracking algorithm development, the basic techniques such as tracking and behavioural semantic labels can be applied over a larger spatial and temporal setting. As an example, in the Columbus Large Image Format (CLIF) data set [2], identified conditions include sensor system performance (camera motion and frame rate, contrast, and camera model fidelity), targets (turning, type, and speed), and environments (shade, occlusion, on and off roads). …”

Section: Wide Area Motion Imagery (Sensors)mentioning

confidence: 99%

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Dismount tracking and identification from electro-optical imagery

Blasch

Ling

et al. 2012

Evolutionary and Bio-Inspired Computation: Theory and Applications VI

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With the advent of new technology in wide-area motion imagery (WAMI) and full-motion video (FMV), there is a capability to exploit the imagery in conjunction with other information sources for improving confidence in detection, tracking, and identification (DTI) of dismounts. Image exploitation, along with other radar and intelligence information can aid decision support and situation awareness. Many advantages and limitations exist in dismount tracking analysis using WAMI/FMV; however, through layered management of sensing resources, there are future capabilities to explore that would increase dismount DTI accuracy, confidence, and timeliness. A layered sensing approach enables commandlevel strategic, operational, and tactical analysis of dismounts to combine multiple sensors and databases, to validate DTI information, as well as to enhance reporting results. In this paper, we discuss WAMI/FMV, compile a list of issues and challenges of exploiting the data for WAMI, and provide examples from recently reported results. Our aim is to provide a discussion to ensure that nominated combatants are detected, the sensed information is validated across multiple perspectives, the reported confidence values achieve positive combatant versus non-combatant detection, and the related situational awareness attributes including behavior analysis, spatial-temporal relations, and cueing are provided in a timely and reliable manner to stakeholders.

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“…Typically, reliability refers to the continuous operation of equipment. In most cases, the use of reliability for decision support refers to the equipment function; however for sensor management (or target classification), reliability can be confused with confidence as per the NATO Standardized Agreements [77,78,79] Credibility provides a soft definition of target identification which is closely related to reliability of which all the definitions are variations of "confidence" as shown below. Note that the MIL-STD-6040 provides quantitative labels.…”

Section: Confidence Versus Reliabilitymentioning

confidence: 99%

Information fusion measures of effectiveness (MOE) for decision support

2011

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For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE) and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for decision support systems. In this paper, we overview some information fusion methods in decision support and address the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an information fusion system. Decision support implies that information methods augment user decision making as opposed to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain, and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy, confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality (time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.

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Video image registration evaluation for a layered sensing environment

Cited by 61 publications

References 17 publications

Overview of contextual tracking approaches in information fusion

Overview of contextual tracking approaches in information fusion

Dismount tracking and identification from electro-optical imagery

Information fusion measures of effectiveness (MOE) for decision support

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