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Video Based Fall Detection using Features of Motion, Shape and Histogram

Published: 26 June 2018 Publication History

Abstract

Falls are one of the greatest risks for the older adults living alone at home. This paper presents a novel visual-based fall detection approach to support independent living for older adults. The proposed approach employs three unique features; motion information, human shape variation and projection histogram to detect a fall. Motion information of a segmented silhouette, which when extracted can provide a useful cue for classifying different behaviours. Also, the projection histogram and variation in human shape can be used to describe human body postures and subsequently fall events. The proposed approach presented here extracts motion information, using best-fit approximated ellipse around the human body and in addition projection histogram features to further improve the accuracy of fall detection. Experimental results are presented and show high fall detection rate of 99.81% with partially occluded video data.

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  • (2022)Fast Shape Recognition Method Using Feature Richness Based on the Walking Minimum Bounding Rectangle over an Occluded Remote Sensing TargetRemote Sensing10.3390/rs1422584514:22(5845)Online publication date: 18-Nov-2022
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PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference
June 2018
591 pages
ISBN:9781450363907
DOI:10.1145/3197768
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 the author(s) 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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  • NSF: National Science Foundation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2018

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Author Tags

  1. Fall detection
  2. assistive care
  3. human shape
  4. motion history
  5. projection histogram

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Cited By

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  • (2023)Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor SafetyElectronics10.3390/electronics1205125912:5(1259)Online publication date: 6-Mar-2023
  • (2022)Interior Distance Ratio to a Regular Shape for Fast Shape RecognitionSymmetry10.3390/sym1410204014:10(2040)Online publication date: 30-Sep-2022
  • (2022)Fast Shape Recognition Method Using Feature Richness Based on the Walking Minimum Bounding Rectangle over an Occluded Remote Sensing TargetRemote Sensing10.3390/rs1422584514:22(5845)Online publication date: 18-Nov-2022
  • (2022)Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA MethodApplied Sciences10.3390/app1214712412:14(7124)Online publication date: 14-Jul-2022
  • (2022)Fall event detection using the mean absolute deviated local ternary patterns and BiLSTMApplied Acoustics10.1016/j.apacoust.2022.108725192(108725)Online publication date: Apr-2022
  • (2021)Human Fall Detection in Surveillance Videos Using Fall Motion Vector ModelingIEEE Sensors Journal10.1109/JSEN.2021.308218021:15(17162-17170)Online publication date: 1-Aug-2021
  • (2021)CAD based product packaging design scheme based on deformed shape image detection and quality assessment2021 6th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES51350.2021.9489229(1184-1187)Online publication date: 8-Jul-2021
  • (2021)A human fall detection framework based on multi-camera fusionJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2021.193869634:6(905-924)Online publication date: 15-Jul-2021
  • (2020)Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With DementiaIEEE Journal of Translational Engineering in Health and Medicine10.1109/JTEHM.2020.29983268(1-9)Online publication date: 2020
  • (2020)Accelerometer-based Human Fall Detection Using Fuzzy Entropy2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ48607.2020.9177577(1-7)Online publication date: Jul-2020
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