# Elderly Fall Detection Using CCTV Cameras under Partial Occlusion of the Subjects Body

arXiv: https://arxiv.org/abs/2208.07291 · HTML: https://mlsanity.com/paper/2208.07291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience](https://mlsanity.com/paper/2004.00797/) (2020)
- [Video Based Fall Detection Using Human Poses](https://mlsanity.com/paper/2107.14633/) (2021)

## Abstract (arXiv)

One of the possible dangers that older people face in their daily lives is falling. Occlusion is one of the biggest challenges of vision-based fall detection systems and degrades their detection performance considerably. To tackle this problem, we synthesize specifically-designed occluded videos for training fall detection systems using existing datasets. Then, by defining a new cost function, we introduce a framework for weighted training of fall detection models using occluded and un-occluded videos, which can be applied to any learnable fall detection system. Finally, we use both a non-deep and deep model to evaluate the effect of the proposed weighted training method. Experiments show that the proposed method can improve the classification accuracy by 36% for a non-deep model and 55% for a deep model in occlusion conditions. Moreover, it is shown that the proposed training framework can also significantly improve the detection performance of a deep network on normal un-occluded samples.