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CRPF-QC: An Efficient CSI Recurrence Plot-Based Framework for Queue Counting | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

Queue counting using WiFi channel state information (CSI) faces challenges due to susceptibility to external factors and relies on ideal testing environments for current ...Show More

Abstract:

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.
Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 19, 01 October 2024)
Page(s): 31699 - 31714
Date of Publication: 26 June 2024

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I. Introduction

With the development of wireless sensing for Artificial Intelligence and Internet of Things (AIoT), WiFi-based intelligent human detection technology has attracted increasing attention recently. Human are progressively using the perception of changes in their surrounding environment to achieve personalized services, such as activity recognition [1], [2], [3], [4], gesture recognition [5], [6], breath detection [7], indoor positioning [8], human body detection [9], etc. These applications have transformed people’s lifestyles, enhancing the overall quality of life. Among them, human behavior recognition, as a hot research topic, has consistently attracted the attention of researchers and holds broad application prospects in areas, such as smart homes, monitoring of the elderly and patients, and public safety.

References

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