# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 1 Introduction  
Audiovisual segments containing advertisements have long been inserted in TV broadcasts and have become pervasive to almost any multimedia content distribution service. In the particular case of television broadcasting, advertisements represent a significant source of revenue, with the ads being intermingled with a channel's linear programming. These ads, while short, may have different duration and visual characteristics, making it difficult to identify common traits. Moreover, there may also be self-advertisements where the channels announce content to come. Several actors in the content distribution and consumption value chain would benefit from being able to automatically identify advertisement segments. Broadcasters and other content providers could benefit from making use of automatic detection modules to identify segments with advertisements, enabling their removal or substitution with others before content repurposing to augment revenues. Companies that bought time for transmitting their ads can use the detection modules to verify that the contracted conditions have been fulfilled by the broadcaster without the need of a cumbersome and costly manual inspection. Even consumers can benefit by having the detection modules running over stored content to remove the advertisements for a more enjoyable experience. The audiovisual richness of advertisements is very large and ever-changing to cope with society and costumers demands, and advertising companies are pressed for new creative ideas. This makes it difficult to identify common traits in advertisements. Moreover, in the particular case of TV broadcasting, both channel characteristics and the wayadvertisements are inserted may vary from channel to channel, and these may also change depending on national context and type of channel (e.g., generic, news, and movies). Over the years, there have been several government initiatives to regulate how advertisements should be inserted in TV linear programming. As a result, some proposals in the literature take advantage of such rules. However, these are often limited to a national scope, change over time, or cease to be adhered to. In 2010, with the aim of standardizing some general features advertisement segments in TV broadcasting should cohere to, the European Union published a set of recommendations that should be followed by the broadcasters of the member states [1]. In this directive, Article 19 indicates that television broadcasters should impose a set of rules on the broadcasted advertisements, so that (1) they are clearly recognizable and distinguishable from editorial content and (2) they should be distinct from other contents by optical and/or acoustic and/or spatial means. While providing base guidelines, the recommendations give room for each member state to define their own set of rules. In line with such recommendations, some broadcasters seek to differentiate linear content from advertisements by introducing a station logo on the transmitted content. This behavior resulted in the proposal of advertisement detection algorithms based on the identification of the channel logo. However, these proposals typically make use of prior knowledge by targeting specific channels or requiring databases of known logos to effectively work. In this paper, we propose an advertisement detector for television content based on logo identification without requiring any prior data. Moreover, the main target scenario also includes the introduction of as little delay as possible in the classification of advertisement segments, encompassing operation over live content. The algorithm automatically identifies suitable regions for the presence of the logo, followed by an automatic online training of a classifier. The contributions of the paper are twofold: First, state-of-the-art proposals were reviewed, as well as corresponding datasets (when available), and additional streams were collected and manually annotated to perform an analysis of different features present.