A Novel Potential Drowning Detection System Based on Millimeter-Wave Radar.

Radar is widely used in human activity recognition because of its powerful micro-doppler feature capture capability and environmental adaptability. In this work, we propose a novel radar-based potential drowning detection system. To enhance the cross-domain fusion efficiency and intra-domain feature learning, we design a two-stage fusion network for the drowning detection system. In the first-stage fusion, we integrate the encoded features of three-domain radar maps along either the temporal or spatial dimension. In the second-stage fusion, we use Attention-LSTM and 1D-CNN to extract deep information from temporal-fused and spatial-fused features, and further combine these features using a trainable weighted average strategy. Based on our proposed novel fusion architecture, fine-grained aquatic human activity recognition is achieved. In the experiments, we collect a nine-class aquatic human activity dataset. The experimental results demonstrate the superiority of the proposed TSFNet over the state-of-the-art models. The dataset and the associated codes are available at: https://github.com/DingdongD/aquatic-activity-dataset.