  Online videos have witnessed an unprecedented growth over the last decade,
owing to wide range of content creation. This provides the advertisement and
marketing agencies plethora of opportunities for targeted advertisements. Such
techniques involve replacing an existing advertisement in a video frame, with a
new advertisement. However, such post-processing of online videos is mostly
done manually by video editors. This is cumbersome and time-consuming. In this
paper, we propose DeepAds -- a deep neural network, based on the simple
encoder-decoder architecture, that can accurately localize the position of an
advert in a video frame. Our approach of localizing billboards in outdoor
scenes using neural nets, is the first of its kind, and achieves the best
performance. We benchmark our proposed method with other semantic segmentation
algorithms, on a public dataset of outdoor scenes with manually annotated
billboard binary maps.
