# Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging

arXiv: https://arxiv.org/abs/2004.08352 · HTML: https://mlsanity.com/paper/2004.08352/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

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

- [Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions](https://mlsanity.com/paper/2301.00114/) (2022)
- [Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos](https://mlsanity.com/paper/2109.04021/) (2021)
- [Vision-based Human Fall Detection Systems using Deep Learning: A Review](https://mlsanity.com/paper/2207.10952/) (2022)
- [Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition](https://mlsanity.com/paper/2302.13434/) (2023)
- [Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall Detection](https://mlsanity.com/paper/2311.02863/) (2023)
- [An LLM-Empowered Low-Resolution Vision System for On-Device Human Behavior Understanding](https://mlsanity.com/paper/2505.01743/) (2025)
- [Thermal Imaging-based Real-time Fall Detection using Motion Flow and Attention-enhanced Convolutional Recurrent Architecture](https://mlsanity.com/paper/2509.16479/) (2025)

## Abstract (arXiv)

Automatic fall detection is a vital technology for ensuring the health and safety of people. Home-based camera systems for fall detection often put people's privacy at risk. Thermal cameras can partially or fully obfuscate facial features, thus preserving the privacy of a person. Another challenge is the less occurrence of falls in comparison to the normal activities of daily living. As fall occurs rarely, it is non-trivial to learn algorithms due to class imbalance. To handle these problems, we formulate fall detection as an anomaly detection within an adversarial framework using thermal imaging. We present a novel adversarial network that comprises of two-channel 3D convolutional autoencoders which reconstructs the thermal data and the optical flow input sequences respectively. We introduce a technique to track the region of interest, a region-based difference constraint, and a joint discriminator to compute the reconstruction error. A larger reconstruction error indicates the occurrence of a fall. The experiments on a publicly available thermal fall dataset show the superior results obtained compared to the standard baseline.