Capsule Network With Multiscale Feature Fusion for Hidden Human Activity Classification
Xiang Wang, Yumiao Wang, Shisheng Guo, Lingjiang Kong, Guolong Cui
Abstract
This article considers the problem of human activity classification behind the walls using ultrawideband (UWB) radar. The complex-valued multiscale feature fusion capsule network (CV-MCNet) is proposed, which consists of a feature extractor, a multiscale feature fusion (MFF) block, and a capsule block. Specifically, the feature extractor with two complex-valued convolutional layers is designed to extract the deep features from the range profiles. Then, the MFF block is developed to enrich the feature representation of the activity. Finally, a capsule block is applied to implicitly encode the spatial relationship among the features in vector form and aggregate the vectors to get accurate classification results. The proposed CV-MCNet is evaluated by real data, and the results show that it achieves better classification performance compared with the deep convolutional neural network (DCNN), convolutional autoencoder (CAE), and complex-valued convolutional neural network (CV-CNN).