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

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

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

Cited By

View all
  • (2022)Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with FusionDiagnostics10.3390/diagnostics1212306012:12(3060)Online publication date: 6-Dec-2022
  • (2022)Fall Detection and Direction Judgment Based on Posture EstimationDiscrete Dynamics in Nature and Society10.1155/2022/83722912022:1Online publication date: 15-Jun-2022
  • (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
  • Show More Cited By

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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
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]

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  • 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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Author Tags

  1. Fall detection
  2. Lucas and Kanade algorithm
  3. consumer health
  4. health informatics
  5. interest point
  6. motion vectors
  7. von Mises distribution

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Cited By

View all
  • (2022)Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with FusionDiagnostics10.3390/diagnostics1212306012:12(3060)Online publication date: 6-Dec-2022
  • (2022)Fall Detection and Direction Judgment Based on Posture EstimationDiscrete Dynamics in Nature and Society10.1155/2022/83722912022:1Online publication date: 15-Jun-2022
  • (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
  • (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

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