CRPF-QC: An Efficient CSI Recurrence Plot-Based Framework for Queue Counting

Yufan Guo

doi:10.1109/JIOT.2024.3419181

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CRPF-QC: An Efficient CSI Recurrence Plot-Based Framework for Queue Counting

2024-10-01

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DOI：10.1109/JIOT.2024.3419181

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Abstract

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Queue counting using WiFi channel state information (CSI) faces challenges due to susceptibility to external factors and relies on ideal testing environments for current methods. We propose an efficient CSI recurrence plot (RP)-based framework for queue counting (CRPF-QC), containing a transformation module and a recognition module. The conversion module transforms the CSI into RP, distinct from traditional models using a single signal point as the unit for feature extraction, utilizing the signal changes at different timestamps as units for feature extraction and effectively preserving the amplitude and phase relationships between any two time points. In the recognition module, the convolutional neural network (CNN) and the long short-term memory (LSTM) network are combined to profoundly understand the internal structure and changes within the image. The proposed integration framework is adept in the automatic extraction of amplitude and phase features, therefore improving image recognition accuracy. Meanwhile, we explore dynamic changes in the queuing crowd detection based on the Fresnel zone theory, identifying individuals' entering and exiting behaviors at different positions within the Fresnel zone and updating the count accordingly, which makes up for the shortcomings of the static model. Intensive evaluations demonstrate that CRPF-QC, employing just two layers of CNN and one layer of LSTM, excels in adapting to dynamic environmental changes, outperforming traditional queue counting methods. Additionally, the dynamic model attains a perfect 100% accuracy in both scenarios.

Keywords:

Feature extraction

Wireless communication

Accuracy

Wireless fidelity

Fresnel reflection

Sensors

Image recognition

Channel state information (CSI)

convolutional neural network (CNN)

images conversion

long short-term memory (LSTM)