Crowd Counting Through Walls Using WiFi

Abstract

Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.

CrossCount: A Deep Learning System for Device-Free Human Counting Using WiFi

“…5a; it is a controlled environment with low multipath where the WiFi link is deployed right in the middle of the room. This is similar to the testbed used in [17]. The second testbed is more complex, as shown in Fig.…”

“…Some previous work, e.g. [13], [17], proposed a human motion model to simulate this task. However, the mathematical model assumes simple motion that does not capture real life complex scenarios, affecting the system accuracy as we quantify in Section IV-D. CROSSCOUNT resolves this challenge by collecting the training samples for only a single user, form which the whole training data for any arbitrary number of humans inside the area of interest is extrapolated.…”

“…Recently, Depatla et al [17] proposed to utilize the WiFi link blockage events as a discriminate feature instead of features depending on the RSS exact values. In particular, the system embeds the inter-arrival times between the link line of sight (LoS) blockages into a renewable stochastic process that models the human motion mathematically.…”

CrowdEstimator

“…The work in [9], people counting are conducted based on RSSIs between a static pair of transmitter and receiver along a line-of-sight path. In [8], the transmitter and receiver are deployed behind the walls, and the RSSIs are used to estimate number of people in the area in-between. Recently, compared to RSSIs, channel state information (CSI) is more sensitive to moving objects and is used for counting people in [30].…”

Crowd data analytics as seen from WiFi

“…An interesting application that can serve for disaster scenarios is the placement of WiFi sensors outside the building to determine the number of people inside [78]. The solution differentiates between devices behind and in front of the wall based on inter-event time differences added by the wall obstruction.…”