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Spatio-Temporal Data Interpretation Based on Perceptional Model

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Advances in Spatio-Temporal Segmentation of Visual Data

Part of the book series: Studies in Computational Intelligence ((SCI,volume 876))

Abstract

This chapter describes approach to interpretation of the heterogeneous data from sensors based on a new perception model, that implement cognitive functions as abstraction of data, tracking context and switching attention. Based on fuzzy L-R numbers, the knowledge presentation and rules engine inference are introduced. The resilient and interoperability of the perception model is shown in two examples of interpretation heterogeneous spatio-temporal data from sensors about the situation at the intersection and about the navigable path between landmarks of the robot route.

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Authors and Affiliations

  1. Ukrainian State University of Railway Transport, 7 Feierbakh Square, Kharkiv, 61050, Ukraine

    Anatolii Kargin & Tetyana Petrenko

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  1. Anatolii Kargin
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  2. Tetyana Petrenko
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Correspondence to Anatolii Kargin .

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Editors and Affiliations

  1. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Vladimir Mashtalir

  2. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Igor Ruban

  3. Faculty of Management Science and Informatics, University of Žilina, Žilina, Slovakia

    Vitaly Levashenko

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Kargin, A., Petrenko, T. (2020). Spatio-Temporal Data Interpretation Based on Perceptional Model. In: Mashtalir, V., Ruban, I., Levashenko, V. (eds) Advances in Spatio-Temporal Segmentation of Visual Data. Studies in Computational Intelligence, vol 876. Springer, Cham. https://doi.org/10.1007/978-3-030-35480-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-35480-0_3

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