Human Activity Detection Based on Parallel AB-TCN Using Micro-Doppler Signatures

The classification and identification of human activities have been increasingly focused on in the fields of human-computer interaction, search and rescue, health detection, and so on. Deep learning methods have been widely employed in target classification recognition. To enhance the classification performance, an attention-mechanism-based two-channel network (AB-TCN) is proposed. In this architecture, the attention module is embedded into the convolutional neural network (CNN) to achieve feature enhancement and redundancy suppression in the spatial and channel domains. Furthermore, the short-window time-frequency image and long-window time-frequency image are separately input into two symmetrical channels for feature extraction and fusion to enhance the differential feature weight of target behavior. The method is simple and easy to implement, with low computational complexity. The experimental results show that the proposed method has higher detection accuracy, and the classification accuracy is increased by more than 5% compared with the traditional neural network architecture.