AMDNet: Adaptive Fall Detection Based on Multi-scale Deformable Convolution Network

Recent studies by the World Health Organization have shown that human falls have become the leading cause of injury and death worldwide. Therefore, human fall detection is becoming an increasingly important research topic. Deep learning models have potential for fall detection, but they face challenges such as limited utilization of global contextual information, insufficient feature extraction, and high computational requirements. These issues constrain the performance of deep learning on human fall detection in terms of low accuracy, poor generalization, and slow inference. To overcome these challenges, this study proposes an Adaptive Multi-scale Detection Network (AMDNet) based on multi-scale deformable convolutions. The main idea of this method is as follows: 1) Introducing an improved multi-scale fusion module, enhances the network's ability to learn object details and semantic features, thereby reducing the likelihood of false negatives and false positives during the detection process, especially for small objects. 2) Using the Wise-IoU v3 with two layers of attention mechanisms and a dynamic non-monotonic FM mechanism as the boundary box loss function of the AMDNet, improves the model's robustness to low-quality samples and enhances the performance of the object detection. This work also proposes a diversified fall dataset that covers as many real-world fall scenarios as possible. Experimental results show that the proposed method outperforms the current state-of-the-art methods on a self-made dataset.