research-article

Human Fall Detection Using Von Mises Distribution and Motion Vectors of Interest Points

Authors:

Abdessamad Youssfi Alaoui,

Azeddine El Hassouny,

Rachid Oulad Haj Thami,

Hamid TairiAuthors Info & Claims

BDCA'17: Proceedings of the 2nd international Conference on Big Data, Cloud and Applications

Article No.: 82, Pages 1 - 5

https://doi.org/10.1145/3090354.3090438

Published: 29 March 2017 Publication History

Abstract

In the field of public health care, fall detection is one of the major problem, especially for elderly persons. For that, an effective surveillance system is a necessity to reduce injuries caused by falls. Our article presents a new method to detect falls. In fact, we used optical flow to calculate motion vectors and statistical distribution named von Mises.

References

[1]

M. Mubashir, L. Shao, L. Seed, "A survey on fall detection: Principles and approaches," Neurocomputing, vol. 100, pp. 144--152, 2013.

Digital Library

[2]

N. Noury, "A smart sensor for the remote follow up of activity and fall detection of the elderly," in Proceedings of the 2nd International IEEE EMBS Special Topic Conference on Microtechnologies in Medicine and Biology, 2002, pp. 314--317.

[3]

Lee T, Mihailidis A: An intelligent emergency response system: preliminary development and testing of automated fall detection. J Telemed Telecare 2005, 11:194--198S.

[4]

Miaou, P. Sung, and C. Huang - "A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information" - Proceedings of the 1st Distributed Diagnosis and Home Healthcare (D2H2) Conference Arlington, USA, April 2-4, 2006.

[5]

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.

[6]

Hazelhoff L, Han J, de With PHN: Video-based fall detection in the home using principal component analysis. In Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems. Edited by Bland-Talon J, Bourennane S, Philips W, Popescu D, Scheunders P. Juan-les-Pins: Springer-Verlag Berlin; 2008:298--309.

Digital Library

[7]

Rougier C, Meunier J, St-Arnaud A, Rousseau J: Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circuits Syst for Video Technol 2011, 21:611--622

Digital Library

[8]

Vishwakarma V, Mandal C, Sural S: Automatic detection of human fall in video. Lect Notes Comput Sci Pattern Recognit Mach Intell 2007, 4815:616--623.

Digital Library

[9]

D. Anderson et al. - "Recognizing Falls from Silhouettes" - [C] Proceedings of the 28th IEEE EMBS Annual International Conference. New York City, USA, Aug 30-Sept 3, 2006:6388--6391.

[10]

C. Harris and M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, pages 147--151, 1988.

[11]

Barron, J. L., and Thacker, N. A. 2004. Tutorial: Computing 2D and 3D Optical Flow. Tech. Rep. 012, Tina Memo.

[12]

B.D. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision", DARPA Image Understanding Workshop, 1981, pp 121--130 (see also IJCAI '81, pp 674--679).

[13]

Jones, T. A. (2006). MATLAB functions to analyze directional (azimuthal) data---I: Single-sample inference. Computers & Geosciences, 32(2), 166--175.

Digital Library

[14]

Wafia Parr Boub erima. Mo dèles de mélange de von Mises-Fisher.Mathématiques générales [math.GM]. Université René Descartes - Paris V; Universit'e Ferhat Abbas (Sétif Algérie), 2013.

[15]

I. Charfi, J. Miteran, J. Dubois, M. Atri, and R. Tourki. Definition and performance evaluation of a robust svm based fall detection solution. In SITIS'12, pages 218--224, 2012.

Digital Library

[16]

Powers, D. M. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.

Cited By

Alanazi TMuhammad G(2022)Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with FusionDiagnostics10.3390/diagnostics1212306012:12(3060)Online publication date: 6-Dec-2022
https://doi.org/10.3390/diagnostics12123060
Yuan CZhang PYang QWang J(2022)Fall Detection and Direction Judgment Based on Posture EstimationDiscrete Dynamics in Nature and Society10.1155/2022/83722912022:1Online publication date: 15-Jun-2022
https://doi.org/10.1155/2022/8372291
Youssfi Alaoui ATabii YOulad Haj Thami RDaoudi MBerretti SPala P(2021)Fall Detection of Elderly People Using the Manifold of Positive Semidefinite MatricesJournal of Imaging10.3390/jimaging70701097:7(109)Online publication date: 6-Jul-2021
https://doi.org/10.3390/jimaging7070109
Show More Cited By

Index Terms

Human Fall Detection Using Von Mises Distribution and Motion Vectors of Interest Points
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Animation
      1. Motion capture
      2. Motion processing

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Other conferences

BDCA'17: Proceedings of the 2nd international Conference on Big Data, Cloud and Applications

March 2017

685 pages

ISBN:9781450348522

DOI:10.1145/3090354

Conference Chairs:
Mohamed Lazaar
ENSA, Tetuan, Morocco
,
Youness Tabii
ENSA, Tetuan, Morocco
,
Mohamed Chrayah
ENSA, Tetuan - Morocco
,
Mohammed Al Achhab
ENSA, Tetuan, Morocco

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Ministère de I'enseignement supérieur: Ministère de I'enseignement supérieur

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 March 2017

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BDCA'17

BDCA'17: 2nd international Conference on Big Data, Cloud and Applications

March 29 - 30, 2017

Tetouan, Morocco

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Cited By

Alanazi TMuhammad G(2022)Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with FusionDiagnostics10.3390/diagnostics1212306012:12(3060)Online publication date: 6-Dec-2022
https://doi.org/10.3390/diagnostics12123060
Yuan CZhang PYang QWang J(2022)Fall Detection and Direction Judgment Based on Posture EstimationDiscrete Dynamics in Nature and Society10.1155/2022/83722912022:1Online publication date: 15-Jun-2022
https://doi.org/10.1155/2022/8372291
Youssfi Alaoui ATabii YOulad Haj Thami RDaoudi MBerretti SPala P(2021)Fall Detection of Elderly People Using the Manifold of Positive Semidefinite MatricesJournal of Imaging10.3390/jimaging70701097:7(109)Online publication date: 6-Jul-2021
https://doi.org/10.3390/jimaging7070109
Alaoui AEl Fkihi SThami R(2019)Fall Detection for Elderly People Using the Variation of Key Points of Human SkeletonIEEE Access10.1109/ACCESS.2019.29465227(154786-154795)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2946522

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