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Real-Time Semantic Segmentation with Label Propagation Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Fall Detection Based on Depth-Data in Practice

Authors : Christopher Pramerdorfer, Rainer Planinc, Mark Van Loock, David Fankhauser, Martin Kampel, Michael Brandstötter

Published in: Computer Vision – ECCV 2016 Workshops

Publisher: Springer International Publishing

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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.

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Metadata
Title
Fall Detection Based on Depth-Data in Practice
Authors
Christopher Pramerdorfer
Rainer Planinc
Mark Van Loock
David Fankhauser
Martin Kampel
Michael Brandstötter
Copyright Year
2016
Book
Computer Vision – ECCV 2016 Workshops

Print ISBN: 978-3-319-48880-6

Electronic ISBN: 978-3-319-48881-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48881-3_14

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