Skip to main content

Advertisement

SpringerLink
Log in

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

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. 1 bit ≡ ln 2 nats

  2. The Wishart distribution has the form:

    $$p\left( {{\mathbf{\Sigma }}^{ - 1} |\alpha ,\beta } \right) = c|{\mathbf{\Sigma }}^{ - 1} |^{\left( {\alpha - d - 1} \right)/2} \exp \left[ { - \frac{1} {2}tr\left( {\mbox{\boldmath$\beta$}{\mathbf{ \Sigma }}^{ - 1} } \right)} \right]$$

    where c is a normalisation factor and α is the degrees of freedom.

  3. Analysis is simplified if the prior is chosen such that the posterior has the same functional form as the prior. The prior and posterior are then said to be conjugate.

References

  1. Barnes NM, Edwards NH, Rose DAD, Garner P (1998) Lifestyle monitoring: technology for supported independence. IEE Comput Control Eng J 9:169-174

    Google Scholar 

  2. Bonner S (1998) Assisted interactive dwelling house: Edinvar housing association smart technology demonstrator and evaluation site. In: Porrero IP, Ballabio E (eds) Improving the quality of life for the European citizen (TIDE: technology for inclusive design and equality). IOS Press, Amsterdam, pp 396–400

    Google Scholar 

  3. Marquis-Faulkes F, McKenna SJ, Gregor P, Newell AF (2003) Gathering the requirements for a fall monitor using drama and video with older people. Technol Disabil (in press)

  4. Porteus J, Brownsell S (2000) Using telecare: exploring technologies for independent living for older people. Report on the Anchor Trust/BT telecare research project, Anchor Trust

    Google Scholar 

  5. Kidd CD, Orr R, Abowd GD, Atkeson CG, Essa IA, MacIntyre B, Mynatt ED, Starner T, Newstetter W (1999) The aware home: a living laboratory for ubiquitous computing research. In: Proceedings of the 2nd international workshop on cooperative buildings, integrating information, organization and architecture,Pittsburgh, pp 191–198

  6. Scuffham P, Chaplin S, Legood R (2003) Incidence and costs of unintentional falls in older people in the United Kingdom. J Epidemiol Community Health 57:740–744

    CAS  PubMed  Google Scholar 

  7. Doughty K (2000) Fall prevention and management strategies based on intelligent detection monitoring and assessment. In: New technologies in medicine for the elderly, Charing Cross hospital, London

  8. Chan M, Bocquet H, Campo E, Pous J (1998) Remote monitoring system to measure indoors mobility and transfer of the elderly. In: Porrero IP, Ballabio E (eds) Improving the quality of life for the European citizen (TIDE: Technology for Inclusive Design and Equality), pp 379–383

  9. SeniorWatch (2001) Fall detector. A case study of the European IST Seniorwatch project IST-1999-29086 http://www.seniorwatch.de

  10. McKenna SJ, Marquis-Faulkes F, Newell AF, Gregor P (2004) Using drama for requirements gathering: a case study on advanced sensors in supportive environments for the elderly. Int J Hum Comput Stud (Submitted)

    Google Scholar 

  11. Bromiley PA, Courtney P, Thacker NA (2002) Design of a visual system for detecting natural events by the use of an independent visual estimate: a human fall detector. In: Christensen HI, Philips PJ (eds) Empirical evaluation methods in computer vision. World Scientific Publishing , 50 Machine Perception and Artificial Intelligence

  12. Sixsmith A, Johnson N (2002) SIMBAD: smart inactivity monitor using array-based detector. Gerontechnology 2(1):110

    Google Scholar 

  13. Buxton H, Gong S (1995) Visual surveillance in a dynamic and uncertain world. Artif Intell 78(1–2):431–459

    Google Scholar 

  14. Howarth RJ, Buxton H (1992) An analogical representation of space and time. Image Vis Comput 10(7):467–478

    Google Scholar 

  15. Ayers D, Shah M (2001) Monitoring human behavior from video taken in an office environment. Image Vis Comput 19(12):833–846

    Google Scholar 

  16. Nguyen N, Bui H, Venkatesh S, West G (2003) Recognising and monitoring high-level behaviours in complex spatial environments. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Madison, pp 620–625

  17. Fernyhough JH, Cohn AG, Hogg D (1996) Generation of semantic regions from image sequences. In: Proceedings of the European conference on computer vision, Cambridge, vol 2, pp 475–484

  18. Johnson N, Hogg D (1996) Learning the distribution of object trajectories for event recognition. Image Vis Comput 14(8):609–615

    Google Scholar 

  19. Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Machine Intell 22(8):747–757

    Google Scholar 

  20. Makris D, Ellis T (2002) Path detection in video surveillance. Image Vis Comput 20(12):895–903

    Google Scholar 

  21. Makris D, Ellis T (2003) Automatic learning of an activity-based semantic scene model. In: Proceedings of the IEEE conference on advanced video and signal based surveillance, Miami

  22. Makris D, Ellis T (2002) Spatial and probabilistic modelling of pedestrian behaviour. In: British machine vision conference, Cardiff, vol 2, pp 557-566

  23. Brand M, Kettnaker V (2000) Discovery and segmentation of activities in video. IEEE Trans Pattern Anal Machine Intell 22(8):844–851

    Google Scholar 

  24. Ferryman J (Ed.) (2002) Proceedings of the 3rd IEEE international workshop on performance evaluation of tracking and surveillance, Copenhagen

  25. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Machine Intell 25(5):564–575

    Google Scholar 

  26. Fuentes LM, Velastin SA (2001) People tracking in indoor surveillance applications. In: Proceedings of the 2nd IEEE workshop on performance evaluation of tracking and surveillance, Hawaii

  27. Needham CJ, Boyle RD (2001) Tracking multiple sports players through occlusion congestion and scale. In: Proceedings of the British machine vision conference, Manchester, vol 1, pp 93–102

  28. Bobick A, Intille S, Davis J, Baird F, Pinhanez C, Campbell L, Ivanov Y, Schutte A, Wilson A (1999) The KidsRoom: a perceptually-based interactive and immersive story environment. PRESENCE: Teleop Virtual Environ 8(4):367–391

  29. Yoda I, Hosotani D, Sakaue K (2004) Ubiquitous stereo vision for controlling safety on platforms in railroad stations. In: Proceedings of the 6th Asian conference on computer vision, Jeju, vol 2, pp 770-776

  30. Kim J-W, Choi K-S, Choi B-D, Ko S-J (2003) Real-time system for counting the number of passing people using a single camera. Pattern Recognition Symposium (DAGM), Magdeburg, Lecture notes in computer science, vol 2781, pp 466–473

  31. Chen X, Yang J (2002) Towards monitoring human activities using an omnidirectional camera. In: Proceedings of the international conference on multimodal interfaces, Pittsburgh, pp 423–428

  32. Krumm J, Harris S, Meyers B, Brummitt B, Hale M, Shafer S (2000) Multi-camera multi-person tracking for EasyLiving. In: Proceedings of the 3rd IEEE workshop on visual surveillance, Dublin, pp 3–10

  33. Birchfield S (1998) Elliptical head tracking using intensity gradients and color histograms. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Santa Barbara

  34. Isard M, Blake A (1998) ICondensation: unifying low-level and high-level tracking in a stochastic framework. In: Proceedings of the European conference on computer vision, vol 1, pp 893–908

  35. Liu F, Lin X, Li SZ, Shi Y (2003) Multi-modal face tracking using Bayesian network. In: Proceedings of the IEEE international workshop on analysis and modeling of faces and gestures, Nice

  36. Nait-Charif H, McKenna SJ (2003) Head tracking and action recognition in a smart meeting room. In: Proceedings of the 4th IEEE international workshop on performance evaluation of tracking and surveillance (PETS-ICVS), Graz

  37. Wren CR, Azarbayejani A, Darrell T, Pentland A (1997) Pfinder: real-time tracking of the human body. IEEE Trans Pattern Anal Machine Intell 19(7):780–785

    Google Scholar 

  38. Blake A, Isard M (2000) Active contours. Springer, Berlin Heidelberg New York

    Google Scholar 

  39. McKenna SJ, Jabri S, Duric Z, Rosenfeld A, Wechsler H (2000) Tracking groups of people. Comput Vis Image Understanding 80(1):42–56

    Google Scholar 

  40. Isard M, Blake A (1996) Contour tracking by stochastic propagation of conditional density. In: Proceedings of the European conference on computer vision, vol 1, pp 343–356

  41. Nait-Charif H, McKenna SJ (2004) Tracking poorly modelled motion using particle filters with iterated likelihood weighting. In: Proceedings of the Asian conference on computer vision (ACCV), Jeju Island, pp 156–161

  42. Pitt M, Shephard N (1999) Filtering via simulation: auxiliary particle filters. J Amer Statist Assoc 94(446):590–599

    MathSciNet  Google Scholar 

  43. Roberts SJ (1997) Parametric and non-parametric unsupervised cluster analysis. Pattern Recognit 30(2):261–272

    Google Scholar 

  44. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39:1–38

    Google Scholar 

  45. Roberts S, Husmeier D, Rezek I, Penny W (1998) Bayesian approaches to Gaussian mixture modelling. IEEE Trans Pattern Anal Machine Intell 20(11):1133–1142

    Google Scholar 

  46. Rissanen J (1978) Modelling by shortest data description. Automatica 14:465–471

    Google Scholar 

  47. Sawhney HS, Ayer S (1996) Compact representations of videos through dominant and multiple motion estimation. IEEE Trans Pattern Anal Machine Intell 18(8):814–830

    Google Scholar 

  48. Walter M, Psarrou A, Gong S (2001) Data driven gesture model acquisition using minimum description length. In: Proceedings of the British machine vision conference, Manchester

  49. Carson C, Belongie S, Greenspan H, Malik J (2002) Blobworld: color- and texture-based image segmentation using EM and its application to image querying and classification. IEEE Trans Pattern Anal Machine Intell 24(8):1026–1038

    Google Scholar 

  50. Greenspan H, Goldberger J, Mayer A (2002) A probabilistic framework for spatio-temporal video representation and indexing. In: Proceedings of the European conference on computer vision, Copenhagen

  51. Hansen MH, Yu B (2001) Model selection and the principle of minimum description length. J Amer Statist Assoc 96(454):746–774

    MathSciNet  Google Scholar 

  52. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464

    Google Scholar 

  53. Gauvain J-L, Lee C-H (1994) Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Trans Speech Audio Process 2(2):291–298

    Google Scholar 

  54. Ormoneit D, Tresp V (1998) Averaging maximum penalized likelihood and Bayesian estimation for improving Gaussian mixture probability density estimates. IEEE Trans Neural Netw 9(4):639–650

    Google Scholar 

  55. Cheeseman P, Stutz J (1996) Bayesian classification (AutoClass): theory and results. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, pp 153–180

    Google Scholar 

  56. Geiger D, Heckerman D (2002) Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. Ann Stat 30:1412-1440

    Google Scholar 

  57. Wallace CS, Dowe DL (2000) MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Stat Comput 10(1):73–83

    Google Scholar 

  58. Figueiredo MAT, Leitao JMN, Jain AK (1999) On fitting mixture models. In: Hancock E, Pellilo M (eds) Energy minimization methods in computer vision and pattern recognition. Springer, Berlin Heidelberg New York, pp 54–69

    Google Scholar 

  59. Hue C, Le Cadre J-P, Pérez P (2002) Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Trans Signal Process 50(2):309–325

    Google Scholar 

  60. Isard M, MacCormick J (2001) BraMBLe: a Bayesian multiple-blob tracker. In: Proceedings of the 8th IEEE international conference on computer vision, Vancouver, vol 2, pp 34–41

  61. Khan Z, Balch T, Dellaert F (2004) An MCMC-based particle filter for tracking multiple interacting targets. In: Proceedings of the European conference on computer vision, Prague, vol 4, pp 279–290

  62. MacCormick J, Blake A (1999) A probabilistic exclusion principle for tracking multiple objects. In: Proceedings of the IEEE international conference on computer vision, Kerkyra, pp 572–587

Download references

Acknowledgments

Dr. Nait Charif was supported by UK EPSRC EQUAL grant GR/R27419/01. The authors are grateful to the reviewers for helpful comments on an earlier version of this paper.

Author information

Authors and Affiliations

  1. Division of Applied Computing, University of Dundee, DD1 4HN, Scotland

    Stephen J. McKenna & Hammadi Nait Charif

Authors
  1. Stephen J. McKenna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hammadi Nait Charif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stephen J. McKenna.

Rights and permissions

Reprints and permissions

About this article

Cite this article

McKenna, S.J., Charif, H.N. Summarising contextual activity and detecting unusual inactivity in a supportive home environment. Pattern Anal Applic 7, 386–401 (2004). https://doi.org/10.1007/s10044-004-0233-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-004-0233-2

Keywords

Search

Navigation