# Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow

arXiv: https://arxiv.org/abs/2104.05744 · HTML: https://mlsanity.com/paper/2104.05744/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Built on by (most cited first, 7 of 7)

- [Vision-based Human Fall Detection Systems using Deep Learning: A Review](https://mlsanity.com/paper/2207.10952/) (2022)
- [A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment](https://mlsanity.com/paper/2508.14203/) (2025)
- [DistillH-Mamba: A Hypergraph-Mamba-Based Knowledge Distillation Model for Efficient Impact Fall Detection](https://mlsanity.com/paper/2607.03156/) (2026)
- [Detecting Abnormal Health Conditions in Smart Home Using a Drone](https://mlsanity.com/paper/2310.05012/) (2023)
- [Computationally efficient non-Intrusive pre-impact fall detection system](https://mlsanity.com/paper/2507.03705/) (2025)
- [Anticipatory Fall Detection in Humans with Hybrid Directed Graph Neural Networks and Long Short-Term Memory](https://mlsanity.com/paper/2509.05337/) (2025)
- [Phase-OTDR Event Detection Using Image-Based Data Transformation and Deep Learning](https://mlsanity.com/paper/2512.05830/) (2025)

## Abstract (arXiv)

Accurate fall detection for the assistance of older people is crucial to reduce incidents of deaths or injuries due to falls. Meanwhile, a vision-based fall detection system has shown some significant results to detect falls. Still, numerous challenges need to be resolved. The impact of deep learning has changed the landscape of the vision-based system, such as action recognition. The deep learning technique has not been successfully implemented in vision-based fall detection systems due to the requirement of a large amount of computation power and the requirement of a large amount of sample training data. This research aims to propose a vision-based fall detection system that improves the accuracy of fall detection in some complex environments such as the change of light condition in the room. Also, this research aims to increase the performance of the pre-processing of video images. The proposed system consists of the Enhanced Dynamic Optical Flow technique that encodes the temporal data of optical flow videos by the method of rank pooling, which thereby improves the processing time of fall detection and improves the classification accuracy in dynamic lighting conditions. The experimental results showed that the classification accuracy of the fall detection improved by around 3% and the processing time by 40 to 50ms. The proposed system concentrates on decreasing the processing time of fall detection and improving classification accuracy. Meanwhile, it provides a mechanism for summarizing a video into a single image by using a dynamic optical flow technique, which helps to increase the performance of image pre-processing steps.