# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 3 Dataset Description  
Despite the existence of advertisement detection proposals in the literature, the corresponding datasets are rarely available, mainly due to intellectual property and legal issues. Therefore, for the purpose of evaluating our proposal, a dataset used in the state-of-the-art was augmented with new captures of recent television content broadcasted during 2019. The assembled dataset is comprised of European broadcast streams captured from Digital Terrestrial Television (DTV) broadcasts and cable operators (the sequences were capture for test and development purposes in the context of the R&D project MOG CLOUDSETUP - No17561, supported by Norte Portugal Regional Operational Programme (NORTE 2020)). It is mostly comprised of streams from general content channel containing different types of programs including news, talk shows, movies, and sports. The streams are long (mostly over 1 h) to incorporate a large number of possible situations, including different types of initial states (program, advertisement, mid-program, and mid-advertisement). Moreover, the dataset was manually annotated to delimit the different segments, to be used as ground truth as well to as associate different audiovisual features to the segments. A general overview of the captured videos is presented in Table 1.  
The main audiovisual characteristics considered can be summarized as follows:  
* Sign Language: the presence of a sign language interpretation;
* Banner/Crawler: the presence of a moving line of text during TV news programs or in other contexts;
* Channel Logo: the presence of the television channel logo;
* Subtitles: the existence of subtitles;
* Other Logos: Any other on screen logo that is different from the channel logo;
* Transition Silences: short pauses with no sound;
* Dialog: multiple speakers in sequence;
* Background Music: the presence of songs or jingles. Table 2 summarizes the presence of these audiovisual characteristics in advertisements and programs, thus providing an indication of their discriminating capabilities. The results show that the channel logo is the visual feature that can be used with greater confidence in deciding if a frame belongs to a program or advertisement segment. Although other features have a more reduced presence in advertisements, such as sign language or subtitles, they are also less present in programs, resulting in a more balanced usage. The presence of black frames was also observed in our analysis. However, they occur in both types of content with seemingly no correlation to the content it divides. This illustrates the reason why their usage in advertisement detection scenarios has deprecated. Furthermore, note that other types of logo may be present in either programs or ads increasing the difficulty and complexity of the problem. Although the audio features appear in significant portions of the content, they appear both in program and advertisement content. This renders their usage more difficult, as a detailed analysis of the changes between their features in advertisement and program is required. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Channel** & **Content** & **Provider** & **Duration** & **Ads Duration** \\ \hline RTP1a & Variety & DTV & 6 h 52 min & 0 h 23 min \\ RTP1b & Variety & DTV & 1 h 10 min & 0 h 11 min \\ RTP2 & Variety & DTV & 8 h 25 min & 0 h 24 min \\ SIC & Variety & DTV & 8 h 37 min & 2 h 18 min \\ TVI & Variety & DTV & 8 h 36 min & 2 h 09 min \\ CMTV HD & Variety & Cable & 0 h 42 min & 0 h 06 min \\ Eurosport 1 HD & Sports & Cable & 0 h 42 min & 0 h 05 min \\ FOX HD & Series & Cable & 1 h 35 min & 0 h 24 min \\ \hline \hline \end{tabular}
\end{table} 
Table 1: General overview of the captured streams. Based on the this analysis, as well as on the state-of-the-art, the current proposal exploits the channel logo as visual feature.