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Falling can have fatal consequences for elderly people especially if thefallen person is unable to call for help due to loss of consciousness or anyinjury. Automatic fall detection systems can assist through prompt fall alarmsand by minimizing the fear of falling when living independently at home.Existing vision-based fall detection systems lack generalization to unseenenvironments 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 HumanFall Detector (SSHFD), a deep learning based framework for automatic falldetection from a single image. This is achieved through two key innovations.First, we present a human pose based fall representation which is invariant toappearance characteristics. Second, we present neural network models for 3dpose estimation and fall recognition which are resilient to missing joints dueto occluded body parts. Experiments on public fall datasets show that ourframework successfully transfers knowledge of 3d pose estimation and fallrecognition learnt purely from synthetic data to unseen real-world data,showcasing its generalization capability for accurate fall detection inreal-world scenarios.

SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience