# ECC: Passenger Counting in the Elevator Using Commodity WiFi
## 4 Results
### Comparison with Other Approaches  
Since there are many device-free crowd counting works, ECC needs to compare with the state-of-art approaches. As the underlying data sample distribution of each approach is unbalanced in the different environments, the F1-score, which is not sensitive to the data sample distribution, is used for evaluation. Then, a set of experiments under the same environment of the above subsection are performed. Figure 17 compares the F1-score of these methods in scene 1. For the M1 [25] and M2 [22] methods, about 50% of the results are accurate due to the RSSI-based schemes performing poorly in multi-path complex environments and scene migration. FreeCount [23] (M3) can achieve more than 60% accuracy because CSI is more sensitive to the diversity of transmission channels than RSSI. Due to M4 [24] not only estimating the number of people but also providing human dynamics monitoring through participant number estimation, its F1-score reaches more than 80%.  
Though the proposed methods in M5 [38] can achieve about 100% estimation accuracy for moving people in indoor environments based on COTS, its estimation accuracy drops to about 90% for relatively static crowds in the elevator. The M6 [39] approach uses the relationship between the variation of CSI and the people number for counting and locating dynamic and static crowds. However, its average accuracy for crowd counting and localization can only reach 77% and 82%, respectively. In addition, most of these methods need people to keep moving, and the maximum crowd resolution is low. Compared with the above methods, the estimation accuracy and crowd resolution of ECC are much higher, which reaches more than 95%. For a more specific comparison, this paper adds some other related algorithms and gives the benchmark table of ECC and each method, including range, accuracy, infrastructure, characteristics, etc., as shown in Table 1. It also can be seen from each indicator of the methods in the table that the comprehensive performance of the ECC system is the best and is suitable for application in the elevator environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Environments** & **Standards** & **Classifier** & **Accuracy Rate** &
\begin{tabular}{c} **Max \# of** \\ **People** \\ \end{tabular} \\ \hline
[25] & indoor, outdoor & D-link WBR-1310 and WLAN card & \(-\) & \(P(e\leq 2)>90\%\) & 20 \\
[22] & bus & ESP8266 battery pack and SD card reader & \(-\) & 67–88\% & 17 \\
[23] & indoor & TPLINK N750 & SVM-TKL & \textgreater{}67–99\% & 7 \\
[24] & indoor & Intel 5300 NICs & Semi-supervised Learning & \textgreater{}90\% & 15 \\
[38] & indoor & Intel 5300 NICs & SVM-Gaussian & \textgreater{}99\% & 8 \\
[39] & indoor & ESP32 modules & RF & 77–82\% & 5 \\
[40] & indoor & Intel 5300 NICs and mini R1C & Deep Learning & \textgreater{}82\% & 5 \\
[41] & indoor & HBE-Zigbex & RF & \textgreater{}77\% & 5 \\ ECC & elevator & Atheros 9300 NICs & RF & \(P(e\leq 2)\geq 95\%\) & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benchmark of indexes for state-of-the-art WiFi-based crowd counting.  
Figure 17: Comparison with different methods.