With the advent of faster internet services and growth of multimedia content, we observe a massive growth in the number of online videos. The users generate these video contents at an unprecedented rate, owing to the use of smart-phones and other hand-held video capturing devices. This creates immense potential for the advertising and marketing agencies to create personalized content for the users. In this paper, we attempt to assist the video editors to generate augmented video content, by proposing candidate spaces in video frames. We propose and release a large-scale dataset of outdoor scenes, along with manually annotated maps for candidate spaces. We also benchmark several deep-learning based semantic segmentation algorithms on this proposed dataset.
@article{arxiv.1903.08943,
title = {The CASE Dataset of Candidate Spaces for Advert Implantation},
author = {Soumyabrata Dev and Murhaf Hossari and Matthew Nicholson and Killian McCabe and Atul Nautiyal and Clare Conran and Jian Tang and Wei Xu and François Pitié},
journal= {arXiv preprint arXiv:1903.08943},
year = {2019}
}
Comments
Published in Proc. International Conference on Machine Vision Applications (MVA), 2019