PerUnet: Deep Signal Channel Attention in Unet for WiFi-Based Human Pose Estimation
Yue Zhou, Aichun Zhu, Caojie Xu, Fangqiang Hu, Yifeng Li
Abstract
Traditional image-based pose estimation methods tend to perform poorly in occlusion and darkness. Thus, researchers established sensor-based pose estimation methods such as radio frequency and infrared to resolve the challenges. Nevertheless, traditional sensors remain the weakness of high cost and low flexibility. In this article, we propose the multimodal network PerUnet to establish a WiFi-based human pose estimation network. Benefitting from the powerful multihead attention mechanism and Unet-like architecture, PerUnet fuses fine-grained pose features and contextual information of WiFi channel state information (CSI) to achieve precise pose estimation. Moreover, we propose the attention-based denoising (ABD) method to overcome traditional filters’ disadvantages and help PerUnet extract pose features from CSI. To evaluate the performance of PerUnet, we establish the novel multimodal dataset Wi-Pose composed of images, CSI, and pose annotations. The experimental results demonstrate that PerUnet achieves a competitive performance of WiFi-based pose estimation on Wi-Pose. To further promote the research on WiFi-based human pose estimation, Wi-Pose has been publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NjtechCVLab/Wi-PoseDataset</uri> .