Toward Accurate Crowd Counting in Large Surveillance Areas Based on Passive WiFi Sensing

Great efforts have been devoted to solving the crowd counting problem based on vision or other fine-grained measurements. Popular vision and WiFi channel state information based approaches, though are able to achieve relatively high accuracy, suffer from limited scalability. In contrast, passive WiFi sensing-based approaches are capable of supporting large surveillance areas, but often rely on certain global linear or approximately linear regression models, which cannot accurately capture the complex mapping relationship between WiFi sensing data and the corresponding crowd count, especially in a large surveillance area during a long period of time. This paper addresses the issue from the following three aspects. Firstly, in order to combat with these coarse-grained regression models, the large surveillance is partitioned into grids, such that either a local linear model or other implicit local models can be built with respect to each grid. Secondly, sequential WiFi spatial-temporal matrix (SWSTM) is defined in alignment with grids to encode the spatial-temporal information of crowds based on passive WiFi localization and a sliding time window mechanism. Thirdly, the spatial-temporal correlations among crowd features of different grids are mined to better regress such local models by using a recurrent neural network (RNN) with SWSTMs as inputs. Extensive experiments are conducted in a real campus road network with an area of about
$4000m^{2}$
, and demonstrate that the proposed method significantly reduces the counting error rate from 22.54% to 13.44% compared to several state-of-the-art methods.