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dc.contributor.authorGetschmann, Christopher
dc.contributor.authorEchtler, Florian
dc.date.accessioned2021-09-17T12:11:48Z
dc.date.available2021-09-17T12:11:48Z
dc.date.issued2021
dc.identifier.issn2196-6826
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/37385
dc.description.abstractData 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.en
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofi-com: Vol. 20, No. 2
dc.subjectcitizen science
dc.subjectobject detection
dc.subjectpedestrian tracking
dc.subjectsmartphone
dc.titleDesPat: Smartphone-Based Object Detection for Citizen Science and Urban Surveysen
dc.typeText/Journal Article
dc.pubPlaceBerlin
mci.reference.pages125-139
dc.identifier.doi10.1515/icom-2021-0012


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