Fall Detection Based on Depth-Data in Practice
Abstract
Falls are a leading cause of accidental deaths among the elderly population. The aim of fall detection is to ensure quick help for fall victims by automatically informing caretakers. We present a fall detection method based on depth-data that is able to detect falls reliably while having a low false alarm rate – not only under experimental conditions but also in practice. We emphasize person detection and tracking and utilize features that are invariant with respect to the sensor position, robust to partial occlusions, and computationally efficient. Our method operates in real-time on inexpensive hardware and enables fall detection systems that are unobtrusive, economic, and plug and play. We evaluate our method on an extensive dataset and demonstrate its capability under practical conditions in a long-term evaluation.
Cite
Text
Pramerdorfer et al. "Fall Detection Based on Depth-Data in Practice." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-48881-3_14Markdown
[Pramerdorfer et al. "Fall Detection Based on Depth-Data in Practice." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/pramerdorfer2016eccv-fall/) doi:10.1007/978-3-319-48881-3_14BibTeX
@inproceedings{pramerdorfer2016eccv-fall,
title = {{Fall Detection Based on Depth-Data in Practice}},
author = {Pramerdorfer, Christopher and Planinc, Rainer and Van Loock, Mark and Fankhauser, David and Kampel, Martin and Brandstötter, Michael},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {195-208},
doi = {10.1007/978-3-319-48881-3_14},
url = {https://mlanthology.org/eccv/2016/pramerdorfer2016eccv-fall/}
}