Abstract
Spatio-temporal trajectories contain implicit knowledge about the movement of individuals, which is relevant for problems in various domains, e.g. animal migration, traffic analysis, security. In this paper we present real-time approaches to segment trajectories into meaningful parts which reflect the underlying typical behaviour or structure. Based on this information atypical behaviour can be identified.
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The funding of this research by DFG, BMBF and the Chinese Scholarship council is gratefully acknowledged.
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Sester, M., Feuerhake, U., Kuntzsch, C. et al. Revealing Underlying Structure and Behaviour from Movement Data. Künstl Intell 26, 223–231 (2012). https://doi.org/10.1007/s13218-012-0180-9
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DOI: https://doi.org/10.1007/s13218-012-0180-9