Detecting fall incidents of the elderly based on human-ground contact areas

Dao Huu Hung, Hideo Saito, Gee Sern Hsu

研究成果: Paper

4 引用 (Scopus)

抜粋

People always make a little contact with the ground during usual activities mainly by feet but often lie completely on the ground after accidental falls. Thus, we propose using Human-Ground Contact Areas (HGCA) to classify human states into standing, sitting and lying states. A fall is defined by a fast change of human states from standing or sitting to lying and continuity in lying state for a sufficient duration. Temporal analyzing human-state transitions is used to discriminate falls from usual events. To measure HGCA, we project foreground of monitored person from one view to another by using homography of the ground between them. Overlap regions between projected foreground and foreground in the latter view that only exist in which people are in contact with the ground, due to plane parallax, are measured as HGCA. We generalize a threshold of HGCA to separate lying states from the others from view-invariant distributions of HGCA with respect to human states. We propose using human state simulation in which camera viewpoints are freely changed to capture 3D human models in various states. Hundreds of images are generated from the simulation as training data to build these distributions. We test our method on 'multiple camera fall dataset' leading to a competitive performance with other methods tested on the same dataset.

元の言語English
ページ516-521
ページ数6
DOI
出版物ステータスPublished - 2013 1 1
イベント2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 - Naha, Okinawa, Japan
継続期間: 2013 11 52013 11 8

Other

Other2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013
Japan
Naha, Okinawa
期間13/11/513/11/8

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • これを引用

    Hung, D. H., Saito, H., & Hsu, G. S. (2013). Detecting fall incidents of the elderly based on human-ground contact areas. 516-521. 論文発表場所 2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013, Naha, Okinawa, Japan. https://doi.org/10.1109/ACPR.2013.124