WiCount-DASL: Domain-Adversarial Semisupervised Learning for Wi-Fi-Based Stationary Crowd Counting.

Abstract

Wi-Fi sensing provides a privacy-preserving and device-free sensing modality for stationary crowd counting with a low deployment cost. However, labeled channel state information (CSI) data are difficult to obtain at scale, and CSI distributions vary significantly across deployment environments, leading to limited generalization. While semisupervised learning (SSL) has shown promise in Wi-Fi-sensing tasks, existing approaches primarily focus on human activity recognition (HAR) or localization and do not effectively address pseudolabel reliability or cross-domain robustness in crowd counting. To address these challenges, we propose a Wi-Fi-based crowd counting via domain-adversarial semisupervised learning (WiCount-DASL), a domain-adversarial SSL framework that jointly leverages limited labeled data and abundant unlabeled data while aligning feature representations across scenarios. The framework incorporates a classwise, adaptive pseudolabel thresholding mechanism and a targeted signal-level augmentation strategy to improve pseudolabel quality and robustness. Extensive experiments across multiple real-world deployment scenarios demonstrate that WiCount-DASL achieves competitive and robust counting accuracy under limited labeled data compared with representative baselines.