ADNet: A Deep Network for Detecting Adverts
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by
Murhaf Hossari, Soumyabrata Dev, Matthew Nicholson, Killian McCabe,
Atul Nautiyal, Clare Conran, Jian Tang, Wei Xu, François Pitié
2018
Abstract
Online video advertising gives content providers the ability to deliver
compelling content, reach a growing audience, and generate additional revenue
from online media. Recently, advertising strategies are designed to look for
original advert(s) in a video frame, and replacing them with new adverts. These
strategies, popularly known as product placement or embedded marketing, greatly
help the marketing agencies to reach out to a wider audience. However, in the
existing literature, such detection of candidate frames in a video sequence for
the purpose of advert integration, is done manually. In this paper, we propose
a deep-learning architecture called ADNet, that automatically detects the
presence of advertisements in video frames. Our approach is the first of its
kind that automatically detects the presence of adverts in a video frame, and
achieves state-of-the-art results on a public dataset.
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