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Contextual Tracking in Surface Applications: Algorithms and Design Examples

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Context-Enhanced Information Fusion

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

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Abstract

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.

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Acknowledgments

This work was supported in part by funding provided by Overwatch Systems and Silver Bullet Solutions through an Office of Naval Research grant.

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Authors and Affiliations

  1. Space Exploration Sector, Johns Hopkins University, Applied Physics Laboratory, Laurel, MD, USA

    Adam M. Fosbury

  2. Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Amherst, NY, USA

    John L. Crassidis

  3. U.S. Army Research Laboratory, Adelphi, MD, USA

    Jemin George

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  1. Adam M. Fosbury
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  2. John L. Crassidis
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  3. Jemin George
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Correspondence to John L. Crassidis .

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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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Fosbury, A.M., Crassidis, J.L., George, J. (2016). Contextual Tracking in Surface Applications: Algorithms and Design Examples. 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_13

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  • DOI: https://doi.org/10.1007/978-3-319-28971-7_13

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