Abstract
Counting the number of people and estimating their walking speeds are essential in crowd control and flow. In this work, we propose a system that uses prevalent Wi-Fi signals to identify the number of people entering and leaving a room through a door. It selects the best subcarrier of Wi-Fi signals and applies the Hampel filter to remove outlier information first. Then, it employs a double threshold method to determine the start and end times of entering or leaving. Afterward, it compares the detected signals with the precollected database using the dynamic time-warping algorithm and determines the number of people. It uses a variance threshold method to identify the states of entering or leaving. It also employs a nonlinear fitting approach to calculate the walking speeds. The experiments show that, in a large empty laboratory, the accuracy rates in determining the number of people are 100% for one person, 81% for two persons, and 95% for three persons. In a small office, the accuracy rates for detecting the number of people are 98% for one or two persons, 82% for three persons, 93% for four, and 75% for five persons. For the walking speed estimation, the accuracy rate for a speed error of less than 0.2410 m/s is 75% for a single person.
Highlights
Counting people and determining their walking speeds and directions can find applications in many areas
The Wi-Fi routers and signals commonly available in a room are utilized for the sensing and counting of people entering and leaving through a door
We select the subcarrier with the largest variance for detection processing. Because it has a largest variance, the chosen subcarrier is more sensitive to the changes in the channel state information (CSI) than other subcarriers
Summary
Counting people and determining their walking speeds and directions can find applications in many areas. Wi-Fi signals propagate everywhere and are reflected or scattered by objects and human bodies They carry information about people and their surroundings, and they can be utilized for sensing and detecting human behaviors and activities. Apply the principle of inverse synthetic aperture radar and use the multiple input multiple output interference techniques to eliminate the reflected signals of stationary targets They propose a method that can identify moving targets and estimate the number of targets. A variance method is proposed to determine the states of entering or leaving It utilizes the differences between the signal variances inside and outside a room—a feature that has not been explored or reported in the literature so far. It leads to a relatively simple computation algorithm that requires only one receiving antenna (unlike the two-antenna approach presented in [28])
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