ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display

ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display

We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has be...

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Bibliographic Details
Main Authors: Gatha Varma, Ritu Chauhan, Eiad Yafi
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Sensors
Subjects:
internet of things (IoT)
cultural heritage
artifact reputation
histogram of oriented gradient (HOG)
clustering
human detection
Online Access:https://www.mdpi.com/1424-8220/21/4/1527

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https://www.mdpi.com/1424-8220/21/4/1527

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