Enhancing Passive Wi-Fi Sensing-Based Crowd Analysis via a Deep Compressed Sensing Approach

Real-time awareness of crowd counts and distribution is vital for applications like crowd management, traffic control, and urban planning. Compared to vision-based methods, passive Wi-Fi sensing-based approaches offer advantages, such as lower deployment costs, broader coverage, and greater scalability, and thus have become prevalent for fine-grained crowd analysis, e.g., estimating mobile device locations and subsequently inferring crowd density maps (CDMs). Therein, substantial localization errors are often incurred due to significant measurement noises, and even worse, the sporadic active scanning introduces sparsity in localization-based density maps (DMs), degrading the accuracy of resulting crowd analysis. To address these challenges, Wi-FDM, a passive Wi-Fi sensing-based framework for fine-grained CDM estimation, is proposed. Specifically, to mitigate the considerable localization errors, two models based on convolutional neural networks are employed to capture the relationship between received signal strength fingerprints and various locations more effectively; to alleviate the sparsity in localization-based DMs, deep compressed sensing networks are developed to reconstruct fine-grained CDMs by recover missing spatial details; to efficiently transfer the CDM reconstruction capabilities developed on semi-synthetic datasets to diverse real-world scenarios, learnable linear and convolutional residual blocks were designed to establish correlations across environments, addressing variations in scene characteristics and improving model generalization. Experimental results on real-world datasets demonstrate Wi-FDM not only surpasses state-of-the-art methods by 41.8% and 29.3% in crowd counts and distribution estimation but is also applicable to both indoor and outdoor scenes, demonstrating its potential for crowd management and analysis.