  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.
