The CASE Dataset of Candidate Spaces for Advert Implantation
This work assists video editors in generating augmented content for advertising, but it is incremental as it focuses on dataset creation and benchmarking without introducing new methods.
The paper tackles the problem of identifying candidate spaces for advert implantation in outdoor video frames by proposing and releasing a large-scale dataset with manually annotated maps, and benchmarks several deep-learning semantic segmentation algorithms on it.
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.