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

TY - JOUR

T1 - Summarising contextual activity and detecting unusual inactivity in a supportive home environment

AU - McKenna, Stephen J.

AU - Nait-Charif, Hammadi

N1 - Copyright 2005 Elsevier B.V., All rights reserved.

PY - 2005/8

Y1 - 2005/8

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/23944492142

U2 - 10.1007/s10044-004-0233-2

DO - 10.1007/s10044-004-0233-2

M3 - Article

AN - SCOPUS:23944492142

SN - 1433-7541

VL - 7

SP - 386

EP - 401

JO - Pattern Analysis and Applications

JF - Pattern Analysis and Applications

IS - 4

ER -