Title: Real-time Vision-based Product Placements in Multimedia Videos
Description: Product placement and embedding marketing are recently used extensively for advertisement in today’s skip-ad generation. In this PhD project, we use computer vision and deep learning techniques to accurately perform product placement in multimedia videos. We intend to use convolutional neural networks for accurately detecting existing adverts in videos, tracking them across image frames, and replacing them with new advertisements for targeted audiences. The designed neural networks will be evaluated on available manually annotated data and synthetic datasets. Such developed techniques will have wide-ranging impacts on a variety of applications, including sports billboard marketing, retail fashion advertising, and amongst others.