Abstract
Video Surveillance is an omnipresent topic when it comes to enhancing security and safety in the intelligent home environments. In this paper we propose a novel method to detect various posture-based events in a typical elderly monitoring application in a home surveillance scenario. These events include normal daily life activities, abnormal behaviors and unusual events. Due to the fact that falling and its physical-psychological consequences in the elderly are a major health hazard, we monitor human activities with a particular interest to the problem of fall detection. Combination of best-fit approximated ellipse around the human body, horizontal and vertical velocities of movement and temporal changes of centroid point, would provide a useful cue for detection of different behaviors. Extracted feature vectors are finally fed to a fuzzy multiclass support vector machine for precise classification of motions and determination of a fall event. Reliable recognition rate of experimental results underlines satisfactory performance of our system.
This is a preview of subscription content, log in via an institution to check access.
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
N Noury, A Fleury, P Rumeau, A Bourke, G Laighin, V Rialle, J Lundy “Fall detection - Principles and Methods” Conf Proc IEEE Eng Med Biol Soc. 2007 ;1 :1663-1666 18002293 (P,S,E,B)
Arie Hans Nasution and S. Emmanuel; Intelligent Video Surveillance for Monitoring Elderly in Home Environments, International Workshop on Multimedia Signal Processing (MMSP), Greece, October 2007.
Sixsmith A, Johnson N. “Smart sensor to detect the falls of the elderly”, IEEE Pervasive Computing, vol. 3, no. 2, pp. 42–47, April-June 2004.
Caroline Rougier, Jean Meunier, Alain St-Arnaud, Jacqueline Rousseau,“Fall Detection from Human Shape and Motion History Using Video Surveillance” Advanced Information Networking and Applications Workshops, 2007, AINAW '07. 21st International Conference on, Vol. 2 (2007), pp. 875-880.
B. T¨oreyin, Y. Dedeoglu, and A. C¸etin.HMM based falling person detection using both audio and video. In IEEE International Workshop on Human-Computer Interaction, Beijing, China, 2005.
S.-G.Miaou, P.-H. Sung, and C.-Y. Huang, “A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information” Proc of Distributed Diagnosis and Home Healthcare(D2H2) Conference, 2006
R. Cucchiara, A.Pratti, and R.Vezani, “An Intelligent Surveillance System for Dangerous Situation Detection in home Environments”, in Intelligenza artificable, vol.1, n.1, pp. 11-15, 2004
T. Lee and A. Mihailidis. An intelligent emergency response system: preliminary development and testing of automated fall detection. Journal of telemedicine and telecare, 11(4):194–198, 2005.
Nait CH, McKenna SJ. “Activity summarisation and fall detection in a supportive home environment”, Int. Conf. on Pattern Recognition (ICPR), 2004.
Tao, J., M.Turjo, M.-F. Wong, M.Wang and Y.-P Tan, “Fall Incidents Detection for Intelligent Video Surveillance” , ICICS2005
C.Rougier, J.Meunier, A. St-Arnaud, J.Rousseau, “Monocular 3D Head Tracking to Detect Falls of Elderly People”, International Conference of IEEE Engineering in Medicine and Biology Society, Sept 2006
Lin,C.-W.,et al.,"Compressed-Domain Fall Incident Detection for Intelligent Home Surveillance”. Proceedings of IEEE International Symposium on Circuits and Systems,ISCAS 2005,2005:p.2781-3784.
L. Wang, From blob metrics to posture classification to activity profiling, Proceedings of the 18th International Conference on Pattern Recognition (ICPR, Poster), IV: 736-739, 2006
A. Madabhushi and J.K. Aggarwal, "A Bayesian approach to human activity recognition," Proc. Second IEEE Workshop on Visual Surveillance, pp.25–32, June 1999.
M.Shakeri, H.Deldari, H.Foroughi, "A Novel Fuzzy Background Subtraction Method Based on Cellular Automata for Urban Traffic Applications", 9th International IEEE Conference on Signal Processing, ICSP'08
S.Abe, S.Singh, Support Vector Machine for Pattern Classification, Springer-Verlag London Limited 2005
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this paper
Cite this paper
Foroughi, H., Alishah, M., Pourreza, H., Shahinfar, M. (2010). Distinguishing Fall Activities using Human Shape Characteristics. In: Iskander, M., Kapila, V., Karim, M. (eds) Technological Developments in Education and Automation. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3656-8_95
Download citation
DOI: https://doi.org/10.1007/978-90-481-3656-8_95
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-3655-1
Online ISBN: 978-90-481-3656-8
eBook Packages: Humanities, Social Sciences and LawEducation (R0)