# Pedestrian Counting Based on Piezoelectric Vibration Sensor
## 6 Conclusions and Future Work  
In this paper we presented a novel device-free walking pedestrian counting approach based on piezoelectric sensors. Our approach can protect the privacy of the pedestrians, because only vibration signals are acquired. The sensors used in our work are much cheaper than the geophone sensors used in previous studies, making our approach more economically viable. Furthermore, our approach does not require a high-density sensor deployment. This means that our system can be easily expanded to cover large areas. Our approach supports that multiple people are walking at the same time with the signals mixed together. Unlike previous approaches [15; 16], it makes no strict requirement about the number of groups of walking people in the detection area. Our approach can detect the  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** \\ \hline
0 Person & \(0.9508\pm 0.1042\) & \(0.9990\pm 0.0014\) & \(0.9717\pm 0.0589\) \\
1 Person & \(0.9988\pm 0.0019\) & \(0.9632\pm 0.0806\) & \(0.9793\pm 0.0440\) \\
2 Persons & \(0.9966\pm 0.0060\) & \(0.9977\pm 0.0018\) & \(0.9971\pm 0.0038\) \\
3 Persons & \(0.9900\pm 0.0178\) & \(0.9685\pm 0.0476\) & \(0.9785\pm 0.0247\) \\
4 Persons & \(0.9732\pm 0.0393\) & \(0.9898\pm 0.0175\) & \(0.9810\pm 0.0206\) \\ Accuracy & & & \(0.9827\pm 0.0253\) \\ Micro Average & \(0.9819\pm 0.0293\) & \(0.9836\pm 0.0256\) & \(0.9815\pm 0.0301\) \\ Macro Average & \(0.9847\pm 0.0212\) & \(0.9827\pm 0.0253\) & \(0.9828\pm 0.0252\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification performance of the DNN. The average and standard deviation (stdev) for each metric are calculated using results from the 5-fold cross-validation.  
Figure 10: Confusion matrix normalized over the 5-fold cross-validation evaluation results.  
number of walking people (up to a maximum of four persons within a 3 m by 3 m area) with an averaged F1-score of over 0.98.  
In the future, we will integrate all the vibration signal-based functional modules [27, 28] into one system. As a whole, the vibration-based system, together with the audio and video-based system, will serve as a perception layer for a privacy-protecting smart city.  
Conceptualization, Y.Y. and T.W.; methodology, Y.Y.; software, Y.Y.; validation, Y.Y. and T.W.; formal analysis, Y.Y.; investigation, Y.Y.; resources, Y.Y.; data curation, Y.Y.; writing--original draft preparation, Y.Y. and T.W.; writing--review and editing, Y.Y., X.Q., S.H., W.H., T.W.; visualization, Y.Y.; supervision, T.W. and W.H.; project administration, T.W.; funding acquisition, T.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Evonik Digital. The APC was funded by the Open Access Publication Fund of the University of Duisburg-Essen.  
We acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen. We acknowledge and thank Evonik Digital for this research work's financial support. We thank [PERSON] for his help in the experiment and data collection.  
The authors declare no conflict of interest.