Find It – An Assistant Home Agent

  • Ângelo CostaEmail author
  • Ester Martinez-Martin
  • Angel P. del Pobil
  • Ricardo Simoes
  • Paulo Novais
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 221)


Cognitive impaired population face with innumerable problems in their daily life. Surprisingly, they are not provided with any help to perform those tasks for which they have difficulties. As a consequence, it is necessary to develop systems that allow those people to live independently and autonomously. Living in a technological era, people could take advantage of the available technology, being provided with some solutions to their needs. This paper presents a platform that assists users with remembering where their possessions are. Mainly, an object recognition process together with an intelligent scheduling applications are integrated in an Ambient Assisted Living (AAL) environment.


Bayesian Network Object Recognition Object Detection Case Base Reasoning Vision Module 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Acampora, G., Loia, V.: A dynamical cognitive multi-agent system for enhancing ambient intelligence scenarios. In: IEEE Int. Conf. on Fuzzy Systems, pp. 770–777 (2009)Google Scholar
  2. 2.
    Ahmed, S., El-Sayed, K., Elhabian, S.: Moving object detection in spatial domain using background removal techniques - state-of-art. Recent Patents on Computer Sciencee 1(1), 32–54 (2008)CrossRefGoogle Scholar
  3. 3.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). CVIU 110(3), 346–359 (2008)Google Scholar
  4. 4.
    Bileschi, S.M., Wolf, L.: A unified system for object detection, texture recognition, and context analysis based on the standard model feature set. In: British Machine Vision Conference, vol. 83, pp. 1–10 (2005)Google Scholar
  5. 5.
    Borotschnig, H., Paletta, L., Prantl, M., Pinz, A.: Appearance-based active object recognition. Image and Vision Computing 18(9), 715–727 (2000)CrossRefGoogle Scholar
  6. 6.
    Ciliberto, C., Pattacini, U., Natale, L., Nori, F., Metta, G.: Reexamining lucas-kanade method for real-time independent motion detection: Application to the icub humanoid robot. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 4154–4160 (2011)Google Scholar
  7. 7.
    Costa, A., Castillo, J.C., Novais, P., Fernández-Caballero, A., Simoes, R.: Sensor-driven agenda for intelligent home care of the elderly. Expert Systems with Applications 39(15), 12,192–12,204 (2012), doi:10.1016/j.eswa, 04.058Google Scholar
  8. 8.
    Costa, A., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic Journal of IGPL 20(4), 689–698 (2012), doi:10.1093/jigpal/jzr021Google Scholar
  9. 9.
    Cristinacce, D., Cootes, T.F.: Boosted regression active shape models. In: British Machine Vision Conference, vol. 2, pp. 880–889 (2007)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)Google Scholar
  11. 11.
    Guido, D.: AP Neuroscience Course (2011),
  12. 12.
    Keysers, D., Deselaers, T., Breuel, T.M.: Optimal geometric matching for patch-based object detection. ELCVIA 6(1), 44–54 (2007)Google Scholar
  13. 13.
    Marques, V., Costa, A., Novais, P.: A dynamic user profiling technique in a AmI environment. In: World Congress on Information and Communication Technologies, pp. 1247–1252. IEEE (2011), doi:10.1109/WICT.2011.6141427Google Scholar
  14. 14.
    Martinez-Martin, E., del Pobil, A.P.: Robust Motion Detection in Real-Life Scenarios, springerbr edn. Springer Briefs in Computer Science. Springer, London (2012), doi:10.1007/978-1-4471-4216-4CrossRefGoogle Scholar
  15. 15.
    Martínez-Martín, E., del Pobil, A.P.: Robust object recognition in an unstructured environment. In: Lee, S., Cho, H., Yoon, K.-J., Lee, J. (eds.) Intelligent Autonomous Systems 12. AISC, vol. 193, pp. 705–714. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Migliore, D.A., Matteucci, M., Naccari, M.: A revaluation of frame difference in fast and robust motion detection. In: 4th ACM Int. Workshop on Video Surveillance and Sensor Networks - VSSN 2006, p. 215. ACM Press, New York (2006)Google Scholar
  17. 17.
    Radde, S., Freitag, B.: Using Bayesian Networks To Infer Product Rankings From User Needs. In: UMAP 2010 Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (2010)Google Scholar
  18. 18.
    Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: CVPR, vol. 2, pp. 994–1000 (2005)Google Scholar
  19. 19.
    Shahbaz Khan, F., Anwer, R.M., van de Weijer, J., Bagdanov, A.D., Vanrell, M., Lopez, A.M.: Color attributes for object detection. In: CVPR, pp. 3306–3313 (2012)Google Scholar
  20. 20.
    Tazari, M.R., Wichert, R., Norgall, T.: Towards a unified ambient assisted living and personal health environment. In: Wichert, R., Eberhardt, B. (eds.) Ambient Assisted Living, vol. 63, pp. 141–155. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Toshev, A., Taskar, B., Daniilidis, K.: Shape-based object detection via boundary structure segmentation. IJCV 99(2), 123–146 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Ughetti, M., Trucco, T., Gotta, D.: Development of agent-based, peer-to-peer mobile applications on android with jade. In: The 2nd Int. Conf. on Mobile Ubiquitous Computing, Systems, Services and Technologies, pp. 287–294 (2008)Google Scholar
  23. 23.
    Urdiales, C., Dominguez, M., de Trazegnies, C., Sandoval, F.: A new pyramid-based color image representation for visual localization. Image and Vision Computing 28(1), 78–91 (2010)CrossRefGoogle Scholar
  24. 24.
    Urtasun, R., Fleet, D.J., Fua, P.: Temporal motion models for monocular and multiview 3d human body tracking. CVIU 104(2-3), 157–177 (2006)Google Scholar
  25. 25.
    Varcheie, P.D.Z., Sills-Lavoie, M., Bilodeau, G.A.: An efficient region-based background subtraction technique. In: Canadian Conference on Computer and Robot Vision, pp. 71–78 (2008)Google Scholar
  26. 26.
    Vardasca, R., Simoes, R.: Needs and opportunities in ambient assisted living in portugal. In: 2nd Int. Living Usability Lab Workshop on AAL Latest Solutions, Trends and Applications, AAL 2012, in Conjunction with BIOSTEC 2012, pp. 100–108 (2012)Google Scholar
  27. 27.
    Watson, I.: An introduction to case-based reasoning. In: Watson, I.D. (ed.) UK CBR 1995. LNCS, vol. 1020, pp. 1–16. Springer, Heidelberg (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Ângelo Costa
    • 1
    Email author
  • Ester Martinez-Martin
    • 2
  • Angel P. del Pobil
    • 2
  • Ricardo Simoes
    • 3
    • 4
    • 5
  • Paulo Novais
    • 1
  1. 1.CCTC - Computer Science and Technology Center, Department of InformaticsUniversity of MinhoBragaPortugal
  2. 2.Robotic Intelligence Lab, Engineering and Computer Science DepartmentJaume-I UniversityCastellónSpain
  3. 3.Institute of Polymers and Composites IPC/I3NUniversity of MinhoGuimarãesPortugal
  4. 4.Life and Health Sciences Research Institute (ICVS), School of Health SciencesUniversity of MinhoBragaPortugal
  5. 5.Polytechnic Institute of Cávado and AveBarcelosPortugal

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