# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 2 Related Research
If no higher correlation is found, the channel pair containing the highest correlation in the training set is used. The second contribution is the use of a logistic regression classifier on the resulting RSSI data. The proposed method was tested on the dataset collected by [PERSON] et al. in [19]. Both the use of channel correlation on the test set as well as the use of a logistic regression classifier improved the robustness to environmental changes. [PERSON] et al. proposed the use of a Passive Infrared (PIR) sensor array above the entrance and exits of buses to count the number of people entering and leaving the bus in order to estimate the total number of people present [21]. The accuracy of their implementation was assessed at 95.5 % when one person was passing under the sensor, but this accuracy was reduced to 70% when four people were entering together. In [22], a solution was proposed to perform people counting in waiting lines using a modular system containing RF transceivers placed on top of retractable belt stanchions, creating small zones (1 m \(\times\) 1 m) of four nodes holding between 0 and 4 people. They achieved a classification accuracy of 98% in a single zone. This accuracy can statistically be extrapolated to 93% for a queue of six units. [PERSON] et al. demonstrated the use of an Impulse Radio Ultra-Wideband (IR-UWB) radar sensor to count people in a subway station [23]. The system uses two narrow beam-width radars in order to detect people crossing two Fields of View, enabling the detection of the direction of travel. They reported an error below 10% for most cases. A vision-based crowd density solution for use in narrow tram and metro environments was proposed by [PERSON] et al. [24]. They proposed a novel Convolutional Neural Network (CNN) model named Coordinate Deformable Net (CDNet) using four sections: feature extraction, a Coordinate Attention Module (CAM), a Deformable Scale-aware Module (DSM), and an output section of dilated convolution layers constructing the crowd density map. They validated their proposed solution using their own collected dataset comprising high-resolution images collected inside tram carariages, as well as the open ShanghaiTech dataset. Their proposed algorithm outperformed common density map algorithms and object detection algorithms in estimating the number of people on both datasets. Our proposed solution differs from the related works in the sense that, rather than trying to compensate for the impact that the presence of a railway vehicle has on our measurements, we aim to identify whether the impact exists and switch between separately trained models accordingly. This approach is more suitable for the use case encountered in public transportation environments, such as subway stations, since the impact of rail vehicles is larger than simply moving a chair. This requires a new selection of links to obtain the new optimal accuracy of the system. This work is the first to validate the use of solely a DFWS system to perform crowd size estimation in public transportation platform environments.