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In this paper, we are interested in the problem of counting a crowd of stationary people (i.e., seated) using a pair of WiFi transceivers. While the people in the crowd are stationary, i.e. with no major body motion except breathing, people do not stay still for a long period of time and frequently engage in small in-place body motions called fidgets (e.g., adjusting their seating position, crossing their legs, checking their phones, etc). In this paper, we propose that the aggregate natural fidgeting and in-place motions of a stationary crowd carry crucial information on the crowd count. We then mathematically characterize the Probability Distribution Function (PDF) of the crowd fidgeting and silent periods (which we can extract from the received WiFi signal) and show their dependency on the total number of people in the area. In developing our mathematical models, we show how our problem of interest resembles a several-decade-old M/G/∞ queuing theory problem, which allows us to borrow mathematical tools from the literature on M/G/∞ queues. We extensively validate our proposed approach with a total of 47 experiments in four different environments (including through-wall settings), in which up to and including N = 10 people are seated. We further test our system in different scenarios, and with different activities, representing various engagement levels of the crowd, such as attending a lecture, watching a movie, and reading. Moreover, we test our proposed system with different number of people seated in several different configurations. Our evaluation results show that our proposed approach achieves a very high counting accuracy, with the estimated number of people being only 0 or 1 off from the true number 96.3% of the time in non-through-wall settings, and 90% of the time in through-wall settings. Our results show the potential of our proposed framework for crowd counting in real-world scenarios.

Counting a stationary crowd using off-the-shelf wifi