University of Oulu

Romaissa B.D., Mourad O., Brahim N., Yazid B. (2021) Vision-Based Fall Detection Using Body Geometry. In: Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_13

Vision-based fall detection using body geometry?

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Author: Romaissa, Beddiar Djamila1,2; Mourad, Oussalah1; Brahim, Nini2;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
2Research Laboratory on Computer Science's Complex Systems, University Laarbi Ben M'hidi, Oum El Bouaghi, Algeria
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022021018497
Language: English
Published: Springer Nature, 2021
Publish Date: 2022-03-05
Description:

Abstract

Falling is a major health problem that causes thousands of deaths every year, according to the World Health Organization. Fall detection and fall prediction are both important tasks that should be performed efficiently to enable accurate medical assistance to vulnerable population whenever required. This allows local authorities to predict daily health care resources and reduce fall damages accordingly. We present in this paper a fall detection approach that explores human body geometry available at different frames of the video sequence. Especially, the angular information and the distance between the vector formed by the head -centroid of the identified facial image- and the center hip of the body, and the vector aligned with the horizontal axis of the center hip, are then used to construct distinctive image features. A two-class SVM classifier is trained on the newly constructed feature images, while a Long Short-Term Memory (LSTM) network is trained on the calculated angle and distance sequences to classify falls and non-falls activities. We perform experiments on the Le2i fall detection dataset and the UR FD dataset. The results demonstrate the effectiveness and efficiency of the developed approach.

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-030-68799-1
ISBN Print: 978-3-030-68798-4
Volume: 12664
Pages: 170 - 185
DOI: 10.1007/978-3-030-68799-1_13
OADOI: https://oadoi.org/10.1007/978-3-030-68799-1_13
Host publication: Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021
Host publication editor: Del Bimbo, Alberto
Cucchiara, Rita
Sclaroff, Stan
Farinella, Giovanni Maria
Mei, Tao
Bertini, Marco
Escalante, Hugo Jair
Vezzani, Roberto
Conference: International Conference on Pattern Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work is partly supported by the Algerian Residential Training Program Abroad Outstanding National Program (PNE) that supported the first author stay at University of Oulu and the European YougRes project (# 823701), which are gratefully acknowledged.
Copyright information: © Springer Nature Switzerland AG 2021. “This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-68799-1_13.