Name
Abstract | ||
|---|---|---|
Advertising in TV shows and movies is expensive and has one of the largest market shares in the entire advertising industry. We address the task of adding a given advertising banner in a given video. In this paper, we propose a new algorithm for processing and replacing advertising banners in videos, which preserves the quality of the original video content. This algorithm allows the given posters to be inserted into a video in a fully automated mode. In order to replace a banner, the algorithm requires only a video and an image of the banner to be inserted. Our algorithm uses computer vision methods for localizing banners on the scene, analyzing, and transforming them. We suggest the approach to create a synthetic dataset for fine-tuning advertising banners detection models. We implement three various methods for the banner localization task and compared the approaches with each other and existing methods. The source code and examples of the algorithm performance are publicly available https://github.com/leonfed/ReAds. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1117/12.2623581 | FOURTEENTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2021) |
Keywords | DocType | Volume |
image processing, video processing, detection, advertisement, homography estimation | Conference | 12084 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valeria Efimova | 1 | 0 | 1.01 |
| Leonid Fedotov | 2 | 0 | 0.34 |
| Viacheslav Shalamov | 3 | 0 | 0.34 |
| Andrey Filchenkov | 4 | 0 | 0.68 |