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Summarising contextual activity and detecting unusual inactivity in a supportive home environment

Summarising contextual activity and detecting unusual inactivity in a supportive home environment

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Original languageEnglish
Pages (from-to)386-401
Number of pages16
JournalPattern Analysis and Applications
Volume7
Issue number4
DOIs
StatePublished - Aug 2005

Abstract

Interpretation of human activity and the detection of associated events are eased if appropriate models of context are available. A method is presented for automatically learning a context-specific spatial model in terms of semantic regions, specifically inactivity zones and entry zones. Maximium a posteriori estimation of Gaussian mixtures is used in conjunction with minumum description length for selection of the number of mixture components. Learning is performed using expectation-maximisation algorithms to maximise penalised likelihood functions that incorporate prior knowledge of the size and shape of the semantic regions. This encourages a one-to-one correspondence between the Gaussian mixture components and the regions. The resulting contextual model enables human-readable summaries of activity to be produced and unusual inactivity to be detected. Results are presented using overhead camera sequences tracked using a particle filter. The method is developed and described within the context of supportive home environments which have as their aim the extension of independent, quality living for older people.

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