Cylindric clock model to represent spatio-temporal trajectories

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

To automatically understand agents' environment and its changes, the study of spatio-temporal relations between the objects evolving in the observed scene is of prime importance. In particular, the temporal aspect is crucial to analyze scene's objects of interest and their trajectories, e.g. to follow their movements, understand their behaviours, etc. in this paper, we propose to conceptualize qualititative spatio-temporal relations in terms of the clock model and extend it to a new spatio-temporal model we call cylindric clock model, in order to effectively perform automated reasoning about the scene and its objects of interest and to improve the modeling of dynamic scenes compared to state-of-art approaches as demonstrated in the carried out experiments. Hence, the new formalisation of the qualitative spatio-temporal relations provides an efficient method for both knowledge representation and information processing of spatio-temporal motion data.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Agents and Artificial Intelligence
Place of PublicationMadeira
PublisherICAART
Pages559-564
Number of pages6
Volume2
ISBN (Print)978-989-758-275-2
DOIs
Publication statusPublished - 2018
EventInternational Conference on Agents and Artificial Intelligence - Funchal, Portugal
Duration: 16 Jan 201818 Jan 2018
Conference number: 10th

Conference

ConferenceInternational Conference on Agents and Artificial Intelligence
Abbreviated titleICAART 2018
CountryPortugal
CityFunchal
Period16/01/1818/01/18

Keywords

  • Spatial and Temporal Reasoning
  • Reasoning about Motion and Change
  • Ontologies of Time and Space-time
  • Temporal Information Extraction
  • Spatio-temporal Knowledge Representation Systems

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  • Cite this

    Olszewska, J. I. (2018). Cylindric clock model to represent spatio-temporal trajectories. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence (Vol. 2, pp. 559-564). ICAART. https://doi.org/10.5220/0006649605590564