DesPat: Smartphone-Based Object Detection for Citizen Science and Urban Surveys

Christopher Getschmann, Florian Echtler*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Data acquisition is a central task in research and one of the largest opportunities for citizen science. Especially in urban surveys investigating traffic and people flows, extensive manual labor is required, occasionally augmented by smartphones. We present DesPat, an app designed to turn a wide range of low-cost Android phones into a privacy-respecting camera-based pedestrian tracking tool to automatize data collection. This data can then be used to analyze pedestrian traffic patterns in general, and identify crowd hotspots and bottlenecks, which are particularly relevant in light of the recent COVID-19 pandemic. All image analysis is done locally on the device through a convolutional neural network, thereby avoiding any privacy concerns or legal issues regarding video surveillance. We show example heatmap visualizations from deployments of our prototype in urban areas and compare performance data for a variety of phones to discuss suitability of on-device object detection for our usecase of pedestrian data collection.
Original languageEnglish
Journali-com
Volume20
Issue number2
Pages (from-to)125-139
Number of pages15
ISSN1618-162X
DOIs
Publication statusPublished - 26 Aug 2021

Bibliographical note

Publisher Copyright:
© 2021 Walter de Gruyter GmbH, Berlin/Boston 2021.

Keywords

  • citizen science
  • object detection
  • pedestrian tracking
  • smartphone

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