Visual Intelligence: A Triple-Attention Network for Robust Fall Detection in Complex Environments
Keywords:
Visual Intelligence, Attention Mechanisms; Assisted living; Fall Detection; Visual sensors; Elderly care; Healthcare monitoring.Abstract
Real-time fall detection in complex environments remains a challenging task due to varying human postures, occlusions, and cluttered scenes. This paper presents Symmetry-Aware Visual Intelligence, a novel triple-attention network built upon an enhanced YOLOv5 backbone to ensure robust detection without sacrificing computational efficiency. Our approach integrates three complementary attention mechanisms: Local Attention in early convolutional layers to emphasize posture-relevant spatial symmetry, Squeeze-and-Excitation (SE) blocks within the backbone to recalibrate channel-wise feature importance, and Efficient Channel Attention (ECA) in the neck for improved multi-scale feature fusion. Together, these modules enhance both spatial precision and contextual awareness. The proposed architecture achieves state-of-the-art results, with mAP scores of 0.914 on the DiverseFall dataset and 0.994 on CAUCAFall, outperforming baseline YOLOv5s by 7.7% and 8.2%, respectively. Notably, it also surpasses YOLOv5x in precision (0.903 vs. 0.769) while maintaining a lightweight design with 80% fewer parameters. Extensive ablation studies validate the contribution of each attention module, and training optimization using SGD at a learning rate of 0.001 ensures convergence. Our model offers a high-performance, efficient solution for fall detection in real-world scenarios with structural complexity.
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