Identifying Candidate Spaces for Advert Implantation

TL;DR

This paper introduces a neural network-based method for automatically identifying suitable spaces in videos for virtual advertising, aiming to streamline the process for video editors and improve ad placement quality.

Contribution

It presents a novel neural network approach for candidate space detection in videos, benchmarking against existing architectures on a large outdoor scene dataset.

Findings

Achieves the best results among tested architectures.

Demonstrates effectiveness in outdoor scene candidate space detection.

First work of its kind in multimedia and augmented reality advertising.

Abstract

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.