Name
Abstract | ||
|---|---|---|
The rapid increase in the number of online videos provides the marketing and advertising agents ample opportunities to reach out to their audience. One of the most widely used strategies is product placement, or embedded marketing, wherein new advertisements are integrated seamlessly into existing advertisements in videos. Such strategies involve accurately localizing the position of the advert in the image frame, either manually in the video editing phase, or by using machine learning frameworks. However, these machine learning techniques and deep neural networks need a massive amount of data for training. In this paper, we propose and release the first large-scale dataset of advertisement billboards, captured in outdoor scenes. We also benchmark several state-of-the-art semantic segmentation algorithms on our proposed dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/qomex.2019.8743280 | 2019 ELEVENTH INTERNATIONAL CONFERENCE ON QUALITY OF MULTIMEDIA EXPERIENCE (QOMEX) |
Keywords | Field | DocType |
advertisement, ALOS dataset, deep learning | Computer science,Segmentation,Image frame,Video editing,Artificial intelligence,Machine learning,Deep neural networks | Journal |
Volume | ISSN | Citations |
abs/1904.07776 | 2372-7179 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soumyabrata Dev | 1 | 62 | 13.94 |
| Murhaf Hossari | 2 | 1 | 1.11 |
| Matthew Nicholson | 3 | 1 | 2.12 |
| Killian McCabe | 4 | 1 | 1.11 |
| Atul Nautiyal | 5 | 1 | 1.11 |
| Clare Conran | 6 | 1 | 2.12 |
| Jian Tang | 7 | 1 | 1.11 |
| Wei Xu | 8 | 1 | 1.11 |
| François Pitié | 9 | 237 | 15.59 |