# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 2 Related Research  
In recent years, different solutions have been proposed to perform crowd counting in dynamic public transportation environments. Within the field of DFWS, efforts have been made to improve Radio Frequency (RF) sensing, making it more robust to environmental changes. [PERSON] et al. proposed a backscattering framework using wideband signals to estimate the number of people (called targets in their work) in the environments [16]. They proposed two different methods to achieve this. The first is an individual-centric method, and the second is a crowd-centric method. The individual-centric method makes use of the additional reflections caused by the introduced targets in order to estimate the positions of the reflections. A people count can be derived from the vector of predicted positions. The crowd-centric method uses feature extraction to extract relevant features from the change in the energy pattern received by the receivers to estimate the number of people in the environment. For the crowd-centric method, energy detector (ED) feature extraction has the best performance and results in a counting error below 2 targets in 88% of cases within a numerical analysis of up to 30 targets. The individual-centric method using a maximum likelihood algorithm resulted in a Root Mean Square Error (RMSE) of 1, outperforming the best algorithm used for the crowd-centric method, ED, resulting in an RMSE of 2 to 10 targets for a spatial density of 0.1 targets per square meter. In [17], a method is proposed for estimating the occupancy of an environment derived from the measured Doppler spectrum. The more people in the environment, the higher the spread in the Doppler spectrum. The relationship between the Doppler spectrum-derived feature and the number of people is not dependent on the environment, and thus, once trained, the model can be used in different environments. The paper demonstrates this system in three different rooms with 0 to 7 people present. Their model was trained using the data from one environment and tested on the other two environments. They achieved an average accuracy of 73% and 63% for classification into five levels. [PERSON] et al. demonstrated the use of radio link RSSI fingerprinting to perform localization in cluttered indoor environments [18]. In the first environment, a one-bedroom apartment (5 m \(\times\) 8 m), they used eight transmitters and eight receivers, leading to a cell size of 0.75 m by 0.75 m, and achieved a cell classification accuracy of 97.2% and an average localization error distance of 0.36 m. The second environment was a commercial office space (10 m \(\times\) 15 m), where they employed 13 transmitters and 9 receivers and achieved a classification accuracy of 93.8% and an average localization error distance of 1.4 m. The degradation of wireless sensing due to changes in the environment has been explored in the context of device-free localization (DFL) by [PERSON] et al. [19]. They performed an analysis of the degradation of fingerprint-based DFL in changing indoor environments. They performed their experiments on the main floor of a house (approximately 84 m\({}^{2}\)) by changing the locations of objects (e.g., chairs, tables, plants) in the environment. They tested different classification algorithms and concluded that the random forest classifier was most robust to changes in the environment during the evaluation phase. Furthermore, they examined the use of multiple radio channels and the option to select channels that improve the robustness to a changing environment and found that using two out of the eight measured channels that had the highest correlation during training further improved the localization accuracy. In [20], [PERSON] et al. improved on the work in [19] by allowing the channel selection to be decided during operation. If a higher Pearson correlation coefficient is found between a pair of channels in the currently received data, the new pair of channels containing the higher correlation will be used.