Research Article
Person-Flow Estimation with Preserving Privacy Using Multiple 3D People Counters
@INPROCEEDINGS{10.1007/978-3-030-76063-2_41, author={Yoshiteru Nagata and Takuro Yonezawa and Nobuo Kawaguchi}, title={Person-Flow Estimation with Preserving Privacy Using Multiple 3D People Counters}, proceedings={Science and Technologies for Smart Cities. 6th EAI International Conference, SmartCity360°, Virtual Event, December 2-4, 2020, Proceedings}, proceedings_a={SMARTCITY}, year={2021}, month={5}, keywords={Person-flow estimation Privacy 3D People Counter}, doi={10.1007/978-3-030-76063-2_41} }- Yoshiteru Nagata
Takuro Yonezawa
Nobuo Kawaguchi
Year: 2021
Person-Flow Estimation with Preserving Privacy Using Multiple 3D People Counters
SMARTCITY
Springer
DOI: 10.1007/978-3-030-76063-2_41
Abstract
The spread of mobile phones made it easy to estimate person-flow for corporate marketing, crowd analysis, and countermeasures for disaster and disease. However, due to recent privacy concerns, regulations have been tightened around the world and most smartphone operating systems have increased privacy protection. To solve this, in this study, we propose the person-flow estimation technique with preserving privacy. We use 3D People Counter which can record only the time and direction of passing people, a person’s height, and walking speed, therefore it preserves privacy from the moment of collecting data. To estimate people’s in-out data, we propose four methods and they use some of the sensor data above in different combinations. We compared these methods and the height-based method could estimate about 79% of the sensor data as in-out data. Additionally, we also created a system to interpolate in-out data into person-flow data and to visualize it. By using this method, we believe that it can be used for the purposes described in the beginning.
