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

Authors: McKenna, S.J. and Nait-Charif, H.

Journal: Pattern Analysis and Applications

Volume: 7

Pages: 386-401

ISSN: 1433-7541

DOI: 10.1007/s10044-004-0233-2

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.

http://www.springerlink.com/content/vu723l0552n632h4/fulltext.pdf

Source: Manual

Preferred by: Hammadi Nait-Charif