3 years ago

ADNet: A Deep Network for Detecting Adverts.

Murhaf Hossari, Soumyabrata Dev, Matthew Nicholson, Killian Mccabe, Atul Nautiyal, Clare Conran, Jian Tang, Wei Xu, François Pitié

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.

Publisher URL: http://arxiv.org/abs/1811.04115

DOI: arXiv:1811.04115v1

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