Online video advertising gives content providers the ability to deliver compelling content, reach a growing audience, and generate additional revenue from online media. Recently, advertising strategies are designed to look for original advert(s) in a video frame, and replacing them with new adverts. These strategies, popularly known as product placement or embedded marketing, greatly help the marketing agencies to reach out to a wider audience. However, in the existing literature, such detection of candidate frames in a video sequence for the purpose of advert integration, is done manually. In this paper, we propose a deep-learning architecture called ADNet, that automatically detects the presence of advertisements in video frames. Our approach is the first of its kind that automatically detects the presence of adverts in a video frame, and achieves state-of-the-art results on a public dataset.
Billboards are placed on the sides of a soccer field for advertisement during match telecast. Unlike regular commercials, which are introduced during a break, on-field billboards appear on the TV screen at uncertain time instances, in different sizes, and also for different durations. Automated processing of soccer telecasts for detection and analysis of such billboards can provide important information on the effectiveness of this mode of advertising. We propose a method in which shot boundaries are first identified and the type of each shot is determined. Frames within each shot are then segmented to locate possible regions of interests (ROIs) - locations in a frame where billboards are potentially present. Finally, we use a combination of local and global features for detecting individual billboards by matching with a set of given templates. In this paper we present a system for the localisation and tracking of billboards in streamed soccer matches. The application area for this research is the delivery of customised content to end users. When international soccer matches are broadcast, the diversity of the audience is very large and advertisers would like to be able to adapt the billboards to the different audiences. By replacing the billboards in the video stream this can be achieved. In order to build a more robust system, photometric invariant features are used. These colour features are less susceptible to the changes in illumination. Sensor noise is dealt with through variable kernel density estimation. Precise visibility measuring of billboard advertising is a key element for organizers and broadcasters to make cost effective their sport live relay. However, this activity currently is very manpower and time consuming as it is manually processed for the moment. In this paper we describe a technique for detection of commercial advertisement in sport TV. Based on some a priori knowledge of sport field and commercial advertisement, our technique makes use of fast Hough transform and text's geometry features in order to extract advertisement from sport TV images. Our experiments show that our technique achieves more than 90 accuracy rate.