Abstract Aging society gives rise to the need of fall detection for the elderly. The interference of the environmental noise and the loss of motion information causing fall detection still challenging. In this work, we present a novel two-stream network, called Flow-pose Net (FP-Net), which integrates the optical flow and human pose information to achieve robust and accurate fall detection in videos. Specifically, we use a human pose estimation model to detect the joints of the human body and design a GCN-based network to learn the body appearance feature from human pose. For motion feature extraction, we estimate optical flow from raw videos and utilize a CNN-based network to learn rich motion feature. Finally, the appearance feature and the motion feature are concatenated and then fed into a classifier to perform the classification of fall. To the best of our knowledge, we are the first to combine the optical flow and the human pose to simultaneously extract motion and appearance features for fall detection. Extensive experiments are conducted on two popular datasets URFD and Le2i, and the results show that our FP-Net achieves the state-of-the-art performance and has high robustness.
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