A Dual Generation Adversarial Network for Human Motion Detection Using Micro-Doppler Signatures

Radar sensors and micro-Doppler signatures have been widely used to recognize human motions. Apart from the motion classification task, human motion detection has attracted much attention as an emerging topic. A majority of existing motion detectors are designed for a specific motion, such as falling. In some scenarios, however, a broader range of human actions is of interest, hence a general motion detector is desired. In this paper, we propose a radar-based motion detection model named dual generative adversarial network (DGN). The proposed model tackles the detection task as a one-class classification problem and is applicable to detecting various motions. Unlike prior fall detection algorithms, which depend on manually collected alien data, the DGN employs a dual generation scheme to automatically produce valid alien samples in both the pixel level and the semantic level. The model is verified on two measured radar datasets containing individual motions and interactive motions, respectively. The experimental results show that our method outperforms other existing models on the human motion detection task.