Privacy preserving detection and classification of playground users

Privacy preserving detection and classification of playground users

Bekker, Bernard (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Embedded and Networked Systems)

Zuniga, Marco (mentor)
Kuipers, F.A. (graduation committee)
Chen, Lydia Y. (graduation committee)

Delft University of Technology

Electrical Engineering | Embedded Systems

2020-05-29

Large cities in the Netherlands, like Rotterdam, have hundreds of playgrounds, but local governments have little information on how children and adults use them. Usage data can help create playgrounds that better fit with the residents' needs by identifying what elements of a playground are the most popular, and which do not get used. The AMS Institute and municipality of Rotterdam have asked us to develop a system to collect usage information of playgrounds without recording personally identifiable information. The system requirements are to record the locations of individual users and to estimate if a user is a child or an adult. It must do this without requiring an external power source and without a broadband internet connection. After considering different types of sensors (including computer vision, radio, sound, and mechanical acoustic signals), We decided to use a mmWave radar sensor due to its ability to provide accurate localization, easy installation, and low energy usage without recording identifiable information. We use a commercially available mmWave radar sensor that we configure to localize people in a 30m by 20m area when placed at the perimeter. We use the radar point cloud output from the device for classification by calculating statistics that we use as features for our classifier. We evaluate classifiers based on SVM, Random Forrest, fully connected and recurrent neural nets. We also analyze different methods for combining radar point clouds captured over time from the same person. We collected 100.000 radar point clouds of adults, children, and bicyclists at real playgrounds, split into a training and validation data set. We show that our SVM classifier achieves an accuracy of 79% on single radar frames, and 93% on the combined results of 10 second long sequences of our validation dataset. The classifier requires <1KB of memory and little processing power. Meaning it can execute on an embedded platform powered by a solar panel.

people detection
classification
mmwave
playgrounds
machine learning
neural network
embedded systems

http://resolver.tudelft.nl/uuid:92b947c2-71e2-45e0-afff-66c21e04d5eb

Student theses

master thesis

© 2020 Bernard Bekker