research-article

Single camera based fall detection using motion and human shape features

Authors:

Viet Anh Nguyen,

Thuy Thi NguyenAuthors Info & Claims

SoICT '16: Proceedings of the 7th Symposium on Information and Communication Technology

Pages 339 - 344

https://doi.org/10.1145/3011077.3011103

Published: 08 December 2016 Publication History

Abstract

Fall detection is important for safety for old people or patients living alone. This paper proposes a framework for indoor fall detection using single camera system. Falls are detected based on the analysis of motion orientation, motion magnitude, and human shape changes. With a deep analysis of characteristics of fall events, we propose improvements for motion orientation estimation, large motion detection and human shape detection using motion histogram images (MHI). Fall detection is then determined by analyzing the speed of changing in motion magnitude, motion orientation and human shape before, during and after the fall. Experiments have been conducted on public datasets Li2e having 221 videos of different living environments with various daily activities. The experimental results show high detection accuracies and very fast processing capability.

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Cited By

Huang XLi XYuan LJiang ZJin HWu WCai RZheng MBai H(2025)SDES-YOLO: A high-precision and lightweight model for fall detection in complex environmentsScientific Reports10.1038/s41598-025-86593-915:1Online publication date: 15-Jan-2025
https://doi.org/10.1038/s41598-025-86593-9
Jaafar FChabchoub MAmeyed D(2024)Detection of Abnormal Activities in a Crowd Video Surveillance using Contextual InformationProceedings of the 2024 9th International Conference on Multimedia and Image Processing10.1145/3665026.3665052(31-38)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3665026.3665052
Paul SZisa AAla Walid MZeem YPaul RHaque MHamid M(2023)Human Fall Detection System using Long-Term Recurrent Convolutional Networks for Next-Generation Healthcare: A Study of Human Motion Recognition2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10308247(1-7)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10308247
Show More Cited By

Index Terms

Single camera based fall detection using motion and human shape features
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics

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cover image ACM Other conferences

SoICT '16: Proceedings of the 7th Symposium on Information and Communication Technology

December 2016

442 pages

ISBN:9781450348157

DOI:10.1145/3011077

General Chairs:
Nguyen Manh Hung
NTT University, Vietnam
,
Huynh Quyet Thang
HUST, Vietnam
,
Program Chairs:
Luc De Raedt
KULeuven, Belgium
,
Yves Deville
UCLouvain, Belgium
,
Marc Bui
EPHE, France
,
Truong Thi Dieu Linh
HUST, Vietnam
,
Publications Chairs:
Dinh Viet Sang
HUST, Vietnam
,
Nguyen Hong Phuong
HUST, Vietnam
,
Nguyen Thi Oanh
HUST, Vietnam

Copyright © 2016 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 December 2016

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SoICT '16

SoICT '16: Seventh International Symposium on Information and Communication Technology

December 8 - 9, 2016

Ho Chi Minh City, Vietnam

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SoICT '16 Paper Acceptance Rate 58 of 132 submissions, 44%;

Overall Acceptance Rate 147 of 318 submissions, 46%

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Citations

Cited By

Huang XLi XYuan LJiang ZJin HWu WCai RZheng MBai H(2025)SDES-YOLO: A high-precision and lightweight model for fall detection in complex environmentsScientific Reports10.1038/s41598-025-86593-915:1Online publication date: 15-Jan-2025
https://doi.org/10.1038/s41598-025-86593-9
Jaafar FChabchoub MAmeyed D(2024)Detection of Abnormal Activities in a Crowd Video Surveillance using Contextual InformationProceedings of the 2024 9th International Conference on Multimedia and Image Processing10.1145/3665026.3665052(31-38)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3665026.3665052
Paul SZisa AAla Walid MZeem YPaul RHaque MHamid M(2023)Human Fall Detection System using Long-Term Recurrent Convolutional Networks for Next-Generation Healthcare: A Study of Human Motion Recognition2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10308247(1-7)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10308247
Tay NConnie TOng TTeoh ATeh P(2023)A Review of Abnormal Behavior Detection in Activities of Daily LivingIEEE Access10.1109/ACCESS.2023.323497411(5069-5088)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3234974
Zeng GZeng BHu H(2023)Real-world efficient fall detection: Balancing performance and complexity with FDGA workflowComputer Vision and Image Understanding10.1016/j.cviu.2023.103832237(103832)Online publication date: Dec-2023
https://doi.org/10.1016/j.cviu.2023.103832
Eraso Guerrero JMuñoz España EMuñoz Añasco M(2022)Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A ReviewTecnura10.14483/22487638.1741326:74(213-236)Online publication date: 25-Sep-2022
https://doi.org/10.14483/22487638.17413
Beddiar DOussalah MNini B(2022)Fall detection using body geometry and human pose estimation in video sequencesJournal of Visual Communication and Image Representation10.1016/j.jvcir.2021.10340782:COnline publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.jvcir.2021.103407
Bonifazi GCorradini EUrsino DVirgili LAnceschi EDe Donato M(2022)A machine learning based sentient multimedia framework to increase safety at workMultimedia Tools and Applications10.1007/s11042-021-10984-z81:1(141-169)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s11042-021-10984-z
Zhou JKomuro T(2022)Detecting Fall Actions of Videos by Using Weakly-Supervised Learning and Unsupervised Clustering LearningAdvances in Visual Computing10.1007/978-3-031-20713-6_24(313-324)Online publication date: 3-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20713-6_24
Iazzi ARziza MOulad Haj Thami R(2021)Fall Detection System-Based Posture-Recognition for Indoor EnvironmentsJournal of Imaging10.3390/jimaging70300427:3(42)Online publication date: 26-Feb-2021
https://doi.org/10.3390/jimaging7030042
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