Abstract
Vision-based homecare system receives increasing research interest owing to its efficiency, portability and low-cost characters. This paper presents a vision-based semi-supervised homecare system to automatically monitor the exceptional behaviors of self-helpless persons in home environment. Firstly, our proposed framework tracks the behavior of surveilled individual using dynamic conditional random field tracker fusion, based on which we extract motion descriptor by Fourier curve fitting to model behavior routines for exception detection. Secondly, we propose a Spatial Field constraint strategy to assist SVM-based exception action decision with a Bayesian inference model. Finally, a novel semi-supervised learning mechanism is also presented to overcome the exhaustive labeling behavior in previous works. Experiments over home environment video dataset with five normal and two exceptional behavior categories shows the advantage of our proposed system comparing with previous works.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lin, C.-W., Ling, Z.-H.: Automatic Fall Incident Detection in Compressed Video for Intelligent Homecare. In: Proceedings of 16th International Conference on ICCCN 2007, pp. 1172–1177, August 13-16 (2007)
Lin, C.-W., Ling, Z.-H., Chang, Y.-C., Kuo, C.J.: Compressed-Domain Fall Incident Detection for Intelligent Home Surveillance. In: IEEE International Symposium on ISCAS 2005, vol. 4, pp. 3781–3784, May 23-26 (2005)
Hammadi, N.-C., McKenna, S.J.: Activity Summarisation and Fall Detection in a Supportive Home Environment. In: IEEE Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), vol. 4, pp. 323–326 (2004)
Bonner, S.: Assisted Interactive Dwelling House: Edinvar Housing Association Smart Technology Demonstrator and Evaluation Site. In: Improving the Quality of Life for the European Citizen (TIDE), pp. 396–400.
Yang, S.-Y., Hsu, C.-T.: A Study of Moving Objects Extraction for Surveillance Videos by Background‐Subtraction Method, Master Thesis. National Tsing Hua University (2006)
Vapnik, V.: The nature of statistical learning theory. Springer, New York (1995)
Chen, D., Yang, J., Malkin, R., Wactlar, H.D.: Detecting Social Interactions of the Elderly in a Nursing Home Environment. ACM Transactions on Multimedia Computing, Communications and Applications 3(1), Article 6 (February 2007)
Berrada, D., Romero, M., Abowd, P.G., Blount, M., Davis, P.J.: Automatic Administration of the Get up and Go test. In: Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments, June 2007, pp. 73–75 (2007)
Tabar, A.M., Keshavarz, A., Aghajan, H.: Smart Home Care Network using Sensor Fusion and Distributed Vision-Based Reasoning. In: Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, pp. 145–154 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, T. et al. (2008). Vision-Based Semi-supervised Homecare with Spatial Constraint. In: Huang, YM.R., et al. Advances in Multimedia Information Processing - PCM 2008. PCM 2008. Lecture Notes in Computer Science, vol 5353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89796-5_43
Download citation
DOI: https://doi.org/10.1007/978-3-540-89796-5_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89795-8
Online ISBN: 978-3-540-89796-5
eBook Packages: Computer ScienceComputer Science (R0)