3 research outputs found
Identifying Candidate Spaces for Advert Implantation
Virtual advertising is an important and promising feature in the area of
online advertising. It involves integrating adverts onto live or recorded
videos for product placements and targeted advertisements. Such integration of
adverts is primarily done by video editors in the post-production stage, which
is cumbersome and time-consuming. Therefore, it is important to automatically
identify candidate spaces in a video frame, wherein new adverts can be
implanted. The candidate space should match the scene perspective, and also
have a high quality of experience according to human subjective judgment. In
this paper, we propose the use of a bespoke neural net that can assist the
video editors in identifying candidate spaces. We benchmark our approach
against several deep-learning architectures on a large-scale image dataset of
candidate spaces of outdoor scenes. Our work is the first of its kind in this
area of multimedia and augmented reality applications, and achieves the best
results.Comment: Published in Proc. IEEE 7th International Conference on Computer
Science and Network Technology, 201