An integrated computer vision based OOH audience measurement system

Jialou Wang*, Honglei Li, Shan Shan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Out of Home (OOH) advertising models through deep learning method with demographical information such as gender, age, etc. While a more comprehensive model would involve fine-tuned information from audience. This paper proposed a subdivided apparel recognition model to enhance the existing audience measurement for OOH. SVM accompanied by Libra RCNN and histogram intersection kernels is adopted alongside advertising board-mounted cameras, which obtain unprocessed data from which gender, age and other demographic features are discerned to determine viewers of particularly clothing advertising. Pervasive adoption for contactless consumer engagement, customised content display and consumer analysis is possible through the amalgamation of results, while audience measurement via digital advertising panels can be more effectively understood by OOH companies and businesses.

Original languageEnglish
Title of host publicationICEB 2020 Proceedings
Place of PublicationAtlanta, US
PublisherAIS Electronic Library
Number of pages6
Publication statusPublished - 5 Dec 2020
Event20th International Conference on Electronic Business, ICEB 2020 - Virtual, Hong Kong, China
Duration: 5 Dec 20208 Dec 2020

Publication series

NameProceedings of the International Conference on Electronic Business (ICEB)
ISSN (Print)1683-0040


Conference20th International Conference on Electronic Business, ICEB 2020
CityVirtual, Hong Kong

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