SKIP: Accurate Fall Detection Based on Skeleton Keypoint Association and Critical Feature Perception

As deep learning technology advances, human fall detection (HFD) leveraging convolutional neural networks (CNNs) has recently garnered significant interest within the research community. However, most existing works ignore the cross-frame association of skeleton keypoints and aggregation of feature representations. To address this, we first introduce an image preprocessing (IPP) module, which enhances the foreground and weakens the background. Diverging from common practices that employ the off-the-shelf detector for target position estimation, our skeleton keypoint detection and association (SKDA) module is designed to detect and cross-frame associate the skeleton keypoints with high affinity. This design reduces the misleading impact of ambiguous detections and ensures the continuity of long-range trajectories. Further, our critical feature perception (CFP) module is crafted to help the model learn more discriminative feature representations for human activity classification. Incorporating these components mentioned above, we introduce SKIP, a novel human fall detection approach, showcasing improved detection precision. Evaluations on the publicly available telecommunication system team v2 (TSTv2) and self-build datasets show SKIP's superior performance.