The ALOS Dataset for Advert Localization in Outdoor Scenes
This addresses the data scarcity problem for marketing and advertising agents using product placement in videos, but it is incremental as it focuses on dataset creation rather than novel methods.
The authors tackled the lack of training data for machine learning models that localize advertisements in outdoor scenes by creating and releasing the first large-scale dataset of advertisement billboards, and they benchmarked several state-of-the-art semantic segmentation algorithms on it.
The rapid increase in the number of online videos provides the marketing and advertising agents ample opportunities to reach out to their audience. One of the most widely used strategies is product placement, or embedded marketing, wherein new advertisements are integrated seamlessly into existing advertisements in videos. Such strategies involve accurately localizing the position of the advert in the image frame, either manually in the video editing phase, or by using machine learning frameworks. However, these machine learning techniques and deep neural networks need a massive amount of data for training. In this paper, we propose and release the first large-scale dataset of advertisement billboards, captured in outdoor scenes. We also benchmark several state-of-the-art semantic segmentation algorithms on our proposed dataset.