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

Pattern Analysis and Applications

Volumn 7, Issue 4, 2005, Pages 386-401

  McKenna, Stephen J ^a   Charif, Hammadi Nait ^a  

^a UNIVERSITY OF DUNDEE   (United Kingdom)

Author keywords

Expectation maximisation; Fall detection; Gaussian mixture models; Inactivity detection; Learning spatial context; Supportive home environments

Indexed keywords

EID: 23944492142     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-004-0233-2     Document Type: Article

References (62)

1. Barnes NM, Edwards NH, Rose DAD, Garner P (1998) Lifestyle monitoring: technology for supported independence. IEE Comput Control Eng J 9:169-174
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
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
5. Kidd CD, Orr R, Abowd GD, Atkeson CG, Essa IA, MacIntyre B, Mynatt ED, Starner T, Newsletter 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
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)
11. Bromiley PA, Courtney P, Thacker N A (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
13. Buxton H, Gong S (1995) Visual surveillance in a dynamic and uncertain world. Artif Intell 78(1-2):431-459
14. Howarth RJ, Buxton H (1992) An analogical representation of space and time. Image Vis Comput 10(7):467-478
15. Ayers D, Shah M (2001) Monitoring human behavior from video taken in an office environment. Image Vis Comput 19(12):833-846
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
19. Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Machine Intell 22(8):747-757
20. Makris D, Ellis T (2002) Path detection in video surveillance. Image Vis Comput 20(12):895-903
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
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
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-modalface tracking using Bayesian network. In: Proceedings of the IEEE international workshop on analysis and modeling of faces and gestures, Nice
36. Nait-Cnarif 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
38. Blake A, Isard M (2000) Active contours. Springer, Berlin Heidelberg New York
39. McKenna SJ, Jabri S, Duric Z, Rosenfeld A, Wechsler H (2000) Tracking groups of people. Comput Vis Image Understanding 80(1):42-56
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
43. Roberts SJ (1997) Parametric and non-parametric unsupervised cluster analysis. Pattern Recognit 30(2):261-272
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
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
46. Rissanen J (1978) Modelling by shortest data description. Automatica 14:465-471
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
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
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
52. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461-464
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
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
55. Cheeseman P, Stutz J (1996) Bayesian classification (Auto-Class): 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
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
57. Wallace CS, Dowe DL (2000) MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Stat Comput 10(1):73-83
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
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
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