Effective Deep-Learning-Based Depth Data Analysis on Low-Power Hardware for Supporting Elderly Care

Christopher Pramerdorfer, Rainer Planinc, Martin Kampel

CVPRW 2020 pp. 1584-1590

doi:10.1109/CVPRW50498.2020.00205 /cvprw/2020/pramerdorfer2020cvprw-effective/

Abstract

We present a detailed technical insight into a commercial vision-based sensor for monitoring residents in elderly care facilities and alerting caretakers in case of dangerous situations such as falls or residents not returning to their beds during nighttime. We focus on aspects that enable deeplearning-based object classification in realtime on low-end ARM-based hardware, which is prerequisite for a solution that is performant yet affordable, low-power, and unobtrusive. To this end, we introduce an efficient vision pipeline that maps the input depth data to concise virtual top-views. These views are then processed by a set of convolutional neural networks, with a scheduler selecting the most appropriate one based on the current operating conditions and available hardware resources. In order to overcome the challenge of acquiring large amounts of training data in this privacy-critical environment, we pretrain these networks on a large set of synthetic depth data. These concepts are general and applicable to similar vision tasks.

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Cite

Text

Pramerdorfer et al. "Effective Deep-Learning-Based Depth Data Analysis on Low-Power Hardware for Supporting Elderly Care." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00205

Markdown

[Pramerdorfer et al. "Effective Deep-Learning-Based Depth Data Analysis on Low-Power Hardware for Supporting Elderly Care." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/pramerdorfer2020cvprw-effective/) doi:10.1109/CVPRW50498.2020.00205

BibTeX

@inproceedings{pramerdorfer2020cvprw-effective,
  title     = {{Effective Deep-Learning-Based Depth Data Analysis on Low-Power Hardware for Supporting Elderly Care}},
  author    = {Pramerdorfer, Christopher and Planinc, Rainer and Kampel, Martin},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {1584-1590},
  doi       = {10.1109/CVPRW50498.2020.00205},
  url       = {https://mlanthology.org/cvprw/2020/pramerdorfer2020cvprw-effective/}
}