| Title |
Providing brands visibility data in live sports videos using deep learning algorithms / |
| Authors |
Gudauskas, Julius |
| Full Text |
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| Is Part of |
CEUR workshop proceedings: IVUS 2024: Information society and university studies 2024: proceedings of the 29th international conference on information society and university studies (IVUS 2023) Kaunas, Lithuania, May 17, 2024 / edited by: I. Veitaitė, A. Lopata, T. Krilavičius, M. Woźniak.. Aachen : CEUR-WS. 2024, vol. 3885, p. 185-194.. ISSN 1613-0073 |
| Keywords [eng] |
brands visibility ; logo detection ; YOLO ; Faster R-CNN |
| 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 time- consuming 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 |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
| CC license |
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