| Title |
Providing brands visibility data in live sports videos using deep learning algorithms / |
| Authors |
Gudauskas, Julius |
| DOI |
10.15388/Proceedings.2024.44 |
| Full Text |
|
| Is Part of |
IVUS2024: 29th international conference "Information society and university studies", Vilnius University, Kaunas Faculty, Kaunas, Lithuania, May 17th, 2024: abstracts.. Vilnius : Vilniaus universiteto leidykla. 2024, p. 27 |
| Abstract [eng] |
In the dynamic landscape of marketing and advertising, assessing brand visibility in live sports events plays a pivotal role in understanding brand exposure and impact. Traditional methods of manual annotation and analysis are timeconsuming and subjective, necessitating automated solutions for efficient and objective evaluation. In this study proposed a novel approach leveraging deep learning algorithms to evaluate brand visibility in live sports videos. This research employs state-of-the-art object detection models, such as YOLO (You Only Look Once) and Faster R-CNN, to detect and localize brand logos within video frames. By training these models on annotated open-source logo datasets, we can extract valuable insights about the brands. The experimental results demonstrate the effectiveness of the proposed methodology in detecting logos and providing a valuable data about the positions for brand owners. |
| Published |
Vilnius : Vilniaus universiteto leidykla |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
| CC license |
|
