SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience
- URL: http://arxiv.org/abs/2004.00797v2
- Date: Fri, 3 Apr 2020 02:45:33 GMT
- Title: SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience
- Authors: Umar Asif, Stefan Von Cavallar, Jianbin Tang, and Stefan Harrer
- Abstract summary: Single Shot Human Fall Detector is a deep learning based framework for automatic fall detection from a single image.
First, we present a human pose based fall representation which is invariant to appearance characteristics.
Second, we present neural network models for 3d pose estimation and fall recognition which are resilient to missing joints due to occluded body parts.
- Score: 10.719603033631952
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Falling can have fatal consequences for elderly people especially if the
fallen person is unable to call for help due to loss of consciousness or any
injury. Automatic fall detection systems can assist through prompt fall alarms
and by minimizing the fear of falling when living independently at home.
Existing vision-based fall detection systems lack generalization to unseen
environments due to challenges such as variations in physical appearances,
different camera viewpoints, occlusions, and background clutter. In this paper,
we explore ways to overcome the above challenges and present Single Shot Human
Fall Detector (SSHFD), a deep learning based framework for automatic fall
detection from a single image. This is achieved through two key innovations.
First, we present a human pose based fall representation which is invariant to
appearance characteristics. Second, we present neural network models for 3d
pose estimation and fall recognition which are resilient to missing joints due
to occluded body parts. Experiments on public fall datasets show that our
framework successfully transfers knowledge of 3d pose estimation and fall
recognition learnt purely from synthetic data to unseen real-world data,
showcasing its generalization capability for accurate fall detection in
real-world scenarios.
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