# Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM

arXiv: https://arxiv.org/abs/2007.07773 · HTML: https://mlsanity.com/paper/2007.07773/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Vision-based Human Fall Detection Systems using Deep Learning: A Review](https://mlsanity.com/paper/2207.10952/) (2022)
- [Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review](https://mlsanity.com/paper/2401.11790/) (2024)
- [A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment](https://mlsanity.com/paper/2508.14203/) (2025)
- [Video-Based Inpatient Fall Risk Assessment: A Case Study](https://mlsanity.com/paper/2106.07565/) (2021)
- [Towards Privacy-Supporting Fall Detection via Deep Unsupervised RGB2Depth Adaptation](https://mlsanity.com/paper/2308.12049/) (2023)
- [SDFA: Structure Aware Discriminative Feature Aggregation for Efficient Human Fall Detection in Video](https://mlsanity.com/paper/2503.07008/) (2025)
- [Cut and Continuous Paste towards Real-time Deep Fall Detection](https://mlsanity.com/paper/2202.10687/) (2022)
- [Computer-Aided Fall Recognition Using a Three-Stream Spatial-Temporal GCN Model with Adaptive Feature Aggregation](https://mlsanity.com/paper/2408.12211/) (2024)
- [Real-time fall detection based on vision for low-power edge platforms](https://mlsanity.com/paper/2607.12909/) (2026)

## Abstract (arXiv)

Fall event detection, as one of the greatest risks to the elderly, has been a hot research issue in the solitary scene in recent years. Nevertheless, there are few researches on the fall event detection in complex background. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method based on deep learning technique can clearly extract the moving object in noise background. We further propose an attention guided Bi-directional LSTM model for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.