Djamila Romaissa Beddiar, Mourad Oussalah, Brahim Nini, Fall detection using body geometry and human pose estimation in video sequences, Journal of Visual Communication and Image Representation, Volume 82, 2022, 103407, ISSN 1047-3203, https://doi.org/10.1016/j.jvcir.2021.103407
Fall detection using body geometry and human pose estimation in video sequences
|Author:||Beddiar, Djamila Romaissa1,2; Oussalah, Mourad1; Nini, Brahim2|
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
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022041228467
|Publish Date:|| 2022-04-12
According to the World Health Organization, falling is a significant health problem that causes thousands of deaths every year. Fall detection and fall prediction tasks enable accurate medical assistance to vulnerable populations whenever required, allowing local authorities to predict daily health care resources and to 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, pose estimation, the angle 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 employed to construct new distinctive image features. A two-class Support Vector Machine (SVM) classifier and a Temporal Convolution Network (TCN) are trained on the newly constructed feature images. At the same time, a Long-Short-Term Memory (LSTM) network is trained on the calculated angle and distance sequences to classify fall and non-fall activities. We perform experiments on the Le2i FD dataset and the UR FD dataset, where we also propose a cross-dataset evaluation. The results demonstrate the effectiveness and efficiency of the developed approach.
Journal of visual communication and image representation
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work is supported by the European Young-sters Resilience through Serious Games, under the Internal Security Fund-Police action: 823701-ISFP-2017-AG-RAD grant, which is gratefully acknowledged.
© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).