Abstract
As the number of monitored individuals rises and multipath effects disrupt signals, existing queue monitoring solutions fail to meet efficiency needs. This paper proposes a passive line-of-sight (LOS) human queue detection method based on channel state information (CSI), namely CBHQD. We present a novel time series crowd detection network (TSCD-Net), incorporating genetic algorithm, LSTM, and FC layers to automatically extract amplitude and phase features from CSI, enhancing the simulation of indoor conditions. The genetic algorithm effectively addresses the challenge of local optima, while the fully connected layers excel in dimension reduction, facilitating the integration of valuable information obtained from LSTM. Additionally, we design the Fresnel zone detection, merging the Fresnel zone model with WiFi to estimate people's walking direction, thereby maximizing the accuracy performance of crowd detection. Lastly, we validate the feasibility and efficiency of our approach in a realistic testbed, demonstrating its suitability for detecting larger numbers of individuals.
Published Version
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