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Multi-scale Activity Estimation with Spatial Abstractions

  • Majd HawaslyEmail author
  • Florian T. Pokorny
  • Subramanian Ramamoorthy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10589)

Abstract

Estimation and forecasting of dynamic state are fundamental to the design of autonomous systems such as intelligent robots. State-of-the-art algorithms, such as the particle filter, face computational limitations when needing to maintain beliefs over a hypothesis space that is made large by the dynamic nature of the environment. We propose an algorithm that utilises a hierarchy of such filters, exploiting a filtration arising from the geometry of the underlying hypothesis space. In addition to computational savings, such a method can accommodate the availability of evidence at varying degrees of coarseness. We show, using synthetic trajectory datasets, that our method achieves a better normalised error in prediction and better time to convergence to a true class when compared against baselines that do not similarly exploit geometric structure.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Majd Hawasly
    • 1
    Email author
  • Florian T. Pokorny
    • 2
  • Subramanian Ramamoorthy
    • 3
  1. 1.FiveAI Inc.EdinburghUK
  2. 2.KTH Royal Institute of TechnologyStockholmSweden
  3. 3.School of InformaticsThe University of EdinburghEdinburghUK

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