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Contextual Tracking Approaches in Information Fusion

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Context-Enhanced Information Fusion

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

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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Acknowledgments

This work is partly supported by the Air Force Office of Scientific Research (AFOSR) under the Dynamic Data Driven Application Systems program and the Air Force Research Lab.

Author information

Authors and Affiliations

  1. Air Force Research Lab, Rome, NY, USA

    Erik Blasch

  2. Sigtem Technology Inc., San Mateo, CA, USA

    Chun Yang

  3. Universidad Carlos III de Madrid, Madrid, Spain

    Jesús García

  4. University of Udine, Udine, Italy

    Lauro Snidaro

  5. Center for Multisource Information Fusion and Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA

    James Llinas

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  1. Erik Blasch
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Corresponding author

Correspondence to Erik Blasch .

Editor information

Editors and Affiliations

  1. Department Mathematics and Computer Science, University of Udine, Udine, Italy

    Lauro Snidaro

  2. University Carlos III, Department of Computer Science and Engg. University Carlos III, Colmenarejo, Spain

    Jesús García

  3. Center Multisource Info. Fusion & Dept, Univ. at Buffalo, of Industrial and Engg Center Multisource Info. Fusion & Dept, Buffalo, New York, USA

    James Llinas

  4. Information Directorate, Air Force Research Laboratory Information Directorate, Rome, New York, USA

    Erik Blasch

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Blasch, E., Yang, C., García, J., Snidaro, L., Llinas, J. (2016). Contextual Tracking Approaches in Information Fusion. In: Snidaro, L., García, J., Llinas, J., Blasch, E. (eds) Context-Enhanced Information Fusion. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-28971-7_4

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