A Bionic Approach to Dynamic, Multimodal Scene Perception and Interpretation in Buildings

Authors: Rosemarie Velik, Dietmar Bruckner

Abstract:

Today, building automation is advancing from simple monitoring and control tasks of lightning and heating towards more and more complex applications that require a dynamic perception and interpretation of different scenes occurring in a building. Current approaches cannot handle these newly upcoming demands. In this article, a bionically inspired approach for multimodal, dynamic scene perception and interpretation is presented, which is based on neuroscientific and neuro-psychological research findings about the perceptual system of the human brain. This approach bases on data from diverse sensory modalities being processed in a so-called neuro-symbolic network. With its parallel structure and with its basic elements being information processing and storing units at the same time, a very efficient method for scene perception is provided overcoming the problems and bottlenecks of classical dynamic scene interpretation systems.

Keywords: building automation, biomimetrics, dynamic scene interpretation, human-like perception, neuro-symbolic networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080973

References:

[1] E. Barnard, B. Palensky, P. Palensky, and D. Bruckner, "Towards Learning 2.0," Proc. I.T. Revolutions, Dec. 2008.
[2] D. Bruckner. Probabilistic Models in Building Automation: Recognizing Scenarios with Statistical Methods, PhD thesis at the Vienna University of Technology, 2007.
[3] T. Bucher, C. Curio, J. Edelbrunner, C. Igel, D. Kastrup, I. Leefken, G. Lorenz, A. Steinhage, and W. von Seelen, "Image Processing and Behavior Planning for Intelligent Vehicles," IEEE Trans. Industrial Electronics, vol. 50, no. 1, pp. 62-75 , Feb 2003, doi:10.1109/TIE.2002.807650.
[4] W. Burgstaller. Interpretation of Situations in Buildings, PhD thesis at the Vienna University of Technology, 2007.
[5] Gemma Calvert, Charles Spencer, and Barry E. Stein, editors. The Handbook of Multisensory Processes. MIT Press, 2004.
[6] M.M. Chun and J.M. Wolfe. Blackwells Handbook of Perception, chapter 9, pp. 272-310. Oxford, 2001.
[7] E.B. Goldstein, Wahrnehmungspsychologie, Spektrum Akademischer Verlag, 2002.
[8] W.A. Gruver, "Intelligent Robotics in Manufacturing, Service, and Rehabilitation: An Overview," IEEE Trans. Industrial Electronics, vol. 41, no. 1, pp. 4-11 , Feb 1994, doi:10.1109/41.281602.
[9] E. Jovanov. "A Model for Consciousness: An Engineering Approach," Brain and Consciousness, ECPD Symposium, pp. 291-295, 1997.
[10] A.R. Luria, The Working Brain - An Introduction in Neuropsychology, Basic Books, 1973.
[11] G.Pratl, Processing and Symbolization of Ambient Sensor Data, PhD thesis at the Vienna University of Technology, 2006.
[12] S. Segvica and S. Ribaric, " Determining the Absolute Orientation in a Corridor Using Projective Geometry and Active Vision," IEEE Trans. Industrial Electronics, vol. 48, no. 3, pp. 696-710 , Jun 2001, doi:10.1109/41.925597.
[13] M. Solms and O. Turnbull, The Brain and the Inner World - An Introduction to the Neuroscience of Subjective Experience, Other Press New York, 2002.
[14] S. Tashiro, T. Murakami, "Step Passage Control of a Power-Assisted Wheelchair for a Caregiver," IEEE Trans. Industrial Electronics, vol. 55, no. 4, pp. 1715-1721 , April 2008, doi:10.1109/TIE.2008.917061.
[15] R. Velik, A Bionic Model for Human-like Machine Perception, PhD thesis at the Vienna University of Technology, 2008.
[16] R. Velik and D. Bruckner, "Neuro-symbolic Networks: Introduction to a New Information Processing Principle," Proc. Conference of Industrial Informatics, Jul. 2008.
[17] S. Wermter, "Hybrid Neural Symbolic Integration," Proc. International Conference on Neural Information Processing Systems (ICONIP -98), Dec. 1998.
[18] P. Wide, "The Electronic Head: A Virtual Quality Instrument" IEEE Trans. Industrial Electronics, vol. 48, no. 4, pp. 766-769 , Aug 2001, doi:10.1109/41.937408.
[19] R. Young, J. Kittler, and J. Matas,"Hypothesis selection for scene interpretation using grammatical models of scene evolution," Proc. Fourteenth International Conference on Pattern Recognition, pp. 16-20, Aug. 1998.