Abstract
Wireless sensing represented by WiFi channel state information (CSI) is now enabling various fields of applications such as person identification, human activity recognition, occupancy detection, localization, and crowd estimation these days. So far, those fields are mostly considered as separate topics in WiFi CSI-based methods, on the contrary, some camera and vision-based crowd estimation systems intuitively estimate both crowd size and location at the same time. Our work is inspired by the idea that WiFi CSI also may be able to perform the same as the camera does. In this paper, we construct <i>Wi-CaL</i>, a simultaneous crowd counting and localization system by using ESP32 modules for WiFi links. We extract several features that contribute to dynamic state (moving crowd) and static state (location of the crowd) from the CSI bundles, then assess our system by both conventional machine learning (ML) and deep learning (DL). As a result of ML-based evaluation, we achieved 0.35 median absolute error (MAE) of counting and 91.4% of localization accuracy with five people in a small-sized room, and 0.41 MAE of counting and 98.1% of localization accuracy with 10 people in a medium-sized room, by leave-one-session-out cross-validation. We compared our result with percentage of non-zero elements metric (PEM), which is a state-of-the-art metric for crowd counting, and confirmed that our system shows higher performance (0.41 MAE, 81.8% of within-1-person error) than PEM (0.62 MAE, 66.5% of within-1-person error).
Highlights
The importance of technological prediction of how people will behave or make a decision has been growing up more and more in our modern society since the human population has gone beyond the range of manual processing
Diverse analytic results were obtained by machine learning with comparisons depending on conditions and parameters, we examined the differences and comparisons with the results by deep learning
Since we found that some features extracted from channel state information (CSI) data generally have the monotonic relationship to people count, machine learning (ML) regressor is used for crowd counting
Summary
The importance of technological prediction of how people will behave or make a decision has been growing up more and more in our modern society since the human population has gone beyond the range of manual processing. The camera and visionbased techniques are intuitively possible in human counting with good accuracy thanks to well-developed head counting and pattern recognition in the images [1]–[3] They have an advantage in estimating an extensive crowd over a huge outdoor area. We address a WiFi CSI-based crowd estimation approach, because our target area is the indoor LoS-link environment. In our previous work [20], we were inspired by the idea that the same thing a camera can do can be performed by wireless sensing, and to the best of our knowledge, it was the first attempt of simultaneous crowd estimation by using WiFi CSI. We propose a method for device-free crowd counting and localization Wi-CaL, and evaluate the system by the experiments with the further enhanced features and more people than the previous work, at two different test areas.
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