›› 2017, Vol. 32 ›› Issue (2): 356-367.doi: 10.1007/s11390-017-1725-z

Special Issue: Artificial Intelligence and Pattern Recognition

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

Automatic Fall Detection Using Membership Based Histogram Descriptors

Mohamed Maher Ben Ismail, Ouiem Bchir   

  1. College of Computer and Information Sciences, King Saud University, Riyadh 11495, KSA
  • Received:2016-02-23 Revised:2016-11-20 Online:2017-03-05 Published:2017-03-05
  • About author:Mohamed Maher Ben Ismail is an assistant professor at the Computer Science Department of the College of Computer and Information Sciences at King Saud University, Riyadh. He received his Ph.D. degree in computer science and engineering from the University of Louisville, KY, USA, in 2011. His research interests include pattern recognition, machine learning, data mining and image processing.
  • Supported by:

    This work was supported by the Research Center of the College of Computer and Information Sciences, King Saud University.

We propose a framework for automatic fall detection based on video visual feature extraction. The proposed approach relies on a membership histogram descriptor that encodes the visual properties of the video frames. This descriptor is obtained by mapping the original low-level visual features to a more discriminative descriptor using possibilistic memberships. This mapping can be summarized in two main phases. The first one consists in categorizing the low-level visual features of the video frames and generating homogeneous clusters in an unsupervised way. The second phase uses the obtained membership degrees generated by the clustering process to compute the membership based histogram descriptor (MHD). For the testing stage, the proposed fall detection approach categorizes unlabeled videos as "Fall" or "Non-Fall" scene using a possibilistic K-nearest neighbors classifier. The proposed approach is assessed using standard videos dataset that simulates patient fall. Also, we compare its performance with that of state-of-the-art fall detection techniques.

[1] U. S. Department of Health and Human Services, U. S. Department of Labor. The future supply of long-term care workers in relation to the aging baby boom generation:Report to Congress, 2003. Washington, DC:Office of the Assistant Secretary for Planning and Evaluation. http:aspe.hhs.gov/daltcp/reports/ltcwork.htm, Dec. 2016.

[2] Vincent G K, Velkoff V A. The next four decades:The older population in the United States:2010 to 2050. Current population reports, Washington, DC:US Census Bureau, 2010, pp.25-1138.

[3] U. S. Census Bureau. 2012 National population projections. Washington, DC:U. S. Census Bureau, 2012. https://www.census.gov/population/projections/data/national/, Feb. 2017.

[4] Feder J, Komisar H L. The Importance of Federal Financing to the Nation's Long-Term Care Safety Net. Washington, DC:Georgetown University, 2012.

[5] Congressional Budget Office. Financing long-term care for the elderly. Congress of the United States, 2004. https://www.cbo.gov/sites/default/files/cbofiles/ftpdocs/-54xx/doc5400/04-26-longtermcare.pdf, Dec. 2016

[6] Kaye H S. Disability rates for working-age adults and for the elderly have stabilized, but trends for each mean different results for costs. Health Aff (Millwood) 2013, 32(1):127-134.

[7] Hijaz F, Afzal N, Ahmad T, Hasan O. Survey of fall detection and daily activity monitoring techniques. In Proc. International Conference on Information and Emerging Technologies, June 2010.

[8] Brulin D, Benezeth Y, Courtial E. Posture recognition based on fuzzy logic for home monitoring of the elderly. IEEE Transactions on Information Technology in Biomedicine, 2012, 16(5):974-982.

[9] Yu X G. Approaches and principles of fall detection for elderly and patient. In Proc. the 10th International Conference on e-health Networking, Applications and Services, July 2008, pp.42-47.

[10] Rougier C, Meunier J. Demo:Fall detection using 3D head trajectory-extracted from a single camera video sequence. In Proc. the 1st International Workshop on Video Processing for Security, June 2006.

[11] Planinc R, Kampel M. Emergency system for elderly-A computer vision based approach. In Lecture Notes in Computer Science 6693, Bravo J, Hervás R, Villarreal V (eds.), Springer-Verlag, 2011, pp.79-83.

[12] Karantonis D M, Narayanan M R, Mathie M, Lovell N H, Celler B G. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Transactions on Information Technology in Biomedicine 2006, 10(1):156-167.

[13] Estudillo-Valderrama M, Roa L M, Reina-Tosina J, Naranjo-Hernández D. Design and implementation of a distributed fall detection systempersonal server. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(6):874-881.

[14] Zigel Y, Litvak D, Gannot I. A method for automatic fall detection of elderly people using floor vibrations and soundproof of concept on human mimicking doll falls. IEEE Transactions on Biomedical Engineering, 2009, 56(12):2858-2867.

[15] Li Y, Ho K C, Popescu M. A microphone array system for automatic fall detection. IEEE Transactions on Biomedical Engineering, 2012, 59(5):1291-1301.

[16] Auvinet E, Multon F, Saint-Arnaud A, Rousseau J, Meunier J. Fall detection with multiple cameras:An occlusionresistant method based on 3-D silhouette vertical distribution. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(2):290-300.

[17] Anderson D, Keller J M, Skubic M, Chen X, He Z H. Recognizing falls from silhouettes. In Proc. the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug. 30-Sept. 3, 2006, pp.6388-6391.

[18] Belshaw M, Taati B, Snoek J, Mihailidis A. Towards a single sensor passive solution for automated fall detection. In Proc. the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug. 30-Sept. 3, 2011, pp.1773-1776.

[19] Yu M, Rhuma A, Naqvi S M, Wang L, Chambers J. A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Transactions on Information Technology in Biomedicine, 2012, 16(6):1274-1286.

[20] Thome N, Miguet S, Ambellouis S. A real-time, multiview fall detection system:A LHMM-based approach. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(11):1522-1532.

[21] Feng W G, Liu R, Zhu M. Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera. Signal, Image and Video Processing, 2014, 8(6):1129-1138.

[22] Mirmahboub B, Samavi S, Karimi N, Shirani S. Automatic monocular system for human fall detection based on variations in silhouette area. IEEE Transactions on Biomedical Engineering, 2013, 60(2):427-436.

[23] Rougier C, Meunier J, St-Arnaud A, Rousseau J. Robust video surveillance for fall detection based on human shape deformation. IEEE Transactions on Circuits and Systems for Video Technology, 2011, 21(5):611-622.

[24] Nasution A H, Emmanuel S. Intelligent video surveillance for monitoring elderly in home environments. In Proc. the 9th Workshop on Multimedia Signal Processing, Oct. 2007, pp.203-206.

[25] Vaidehi V, Ganapathy K, Mohan K, Aldrin A, Nirmal K Video based automatic fall detection in indoor environment. In Proc. International Conference on Recent Trends in Information Technology, June 2011, pp.1016-1020.

[26] Vallejo M, Isaza C V, Lopez J D. Artificial neural networks as an alternative to traditional fall detection methods. In Proc. the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, July 2013, pp.1648-1651.

[27] Huang B, Tian G H, Li X L. A method for fast fall detection. In Proc the 7th World Congress on Intelligent Control and Automation, June 2008, pp.3619-3623.

[28] Foroughi H, Rezvanian A, Paziraee A. Robust fall detection using human shape and multi-class support vector machine. In Proc. the 6th Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, Dec. 2008.

[29] Foroughi H, Naseri A, Saberi A, Yazd H S. An eigenspacebased approach for human fall detection using integrated time motion image and neural network. In Proc. the 9th IEEE International Conference on Signal Processing, Oct. 2008, pp.1499-1503.

[30] Shi G Y, Chan C S, Li W J, Leung K S, Zou Y X, Jin Y F. Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier. IEEE Sens. J., 2009, 9(5):495-503.

[31] Fu Z M, Delbruck T, Lichtsteiner P, Culurciello E. An address event fall detector for assisted living applications. IEEE Transactions on Biomedical Circuits and Systems, 2008, 2(2):88-96.

[32] Jiang M, Chen Y Y, Zhao Y Y, Cai A N. A real-time fall detection system based on HMM and RVM. In Proc. Visual Communications and Image Processing, Nov. 2013.

[33] Bobick A F, Davis J W. The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(3):257-267.

[34] Harris Z S. Distributional structure. Word, 1954, 10(2/3):146-162.

[35] Sivic J, Zisserman A. Efficient visual search of videos cast as text retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(4):591-605.

[36] McQueen J. Some methods for classification and analysis of multivariate observations. In Proc. the 5th Berkeley Symposium on Mathematical Statistics and Probability, June 21-July 18, 1967, pp.281-297.

[37] Krishnapuram R, Keller J M. A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1993, 1(2):98-110.

[38] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2005, pp.886-893.

[39] Banerjee A, Dave R N. Validating clusters using the Hopkins statistic. In Proc. IEEE International Conference on-Fuzzy Systems, July 2004, pp.149-153.

[40] Altman N S. An introduction to kernel and nearestneighbor nonparametric regression. The American Statistician, 1992, 46(3):175-185.

[41] Char I, Mitran J, Dubois J, Atri M, Tourki R. Definition and performance evaluation of a robust SVM based fall detection solution. In Proc. the 8th International Conference on Signal Image Technology and Internet Based Systems, Nov. 2012, pp.218-224.

[42] Lam L, Suen C Y. Optimal combinations of pattern classifiers. Pattern Recognition Letters, 1995, 16(9):945-954.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[2] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[3] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Min Yinghua; Han Zhide;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .
[7] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] Zhu Hong;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .
[10] Li Minghui;. CAD System of Microprogrammed Digital Systems[J]. , 1987, 2(3): 226 -235 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

Home
Editorial Board
Author Guidelines
Subscription
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved