# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 5 Evaluation and Results  
To objectively evaluate the proposed method, widely accepted metrics and approaches in the literature were employed. Given that the overall detector assigns a binary result for each processed frame, indicating if it either belongs to an advertisement or to a program, a binary classification evaluation was applied through the calculation of the Precision (Prec), Recall (also known as true positive rate, TPR), F1-Score (F1), and Accuracy (Acc) metrics [31]. For this, the following measures were used:  
* True Positive (TP): Algorithm assigns the tag advertisement to a frame that belongs to an advertisement. * False Positive (FN): Algorithm assigns the tag program to a frame that belongs to an advertisement. * False Negative (FP): Algorithm assigns the tag advertisement to a frame that belongs to a program. * True Negative (TN): Algorithm assigns the tag program to a frame that belongs in fact to a program. Table 5 summarizes the results in terms of precision, recall, F1, and accuracy for the full streams of the datasets. The lowest results depicted in Table 5 can be easily explained due to abnormal occurrences in the streams, such as logo absorption with the background for very long periods of time, as illustrated in Figure 9. In this case, the logo was absorbed by the background for -23% of the whole stream. This results in erroneous classifications of frames as being advertisements when in fact they belong to programs with no visible or perceived logo. Overall, results convey a significant performance with the proposed method being able to adapt to different channels and self-learn how to detect the channel logo and subsequently distinguish advertisements and program content. Most of the results show high accuracy with values above 80%. Figures 10 and 11 provide a visual perspective on the results for the RTP2 and SIC streams. These depict, in a vertical stack, the plotting of the errors, the annotations, and obtained results. The blue bars indicate programming content and the yellow bars advertisements. The abnormal occurrences in the RTP2 are also visible in the temporal representation of the classifications illustrated in Figure 10. As can be seen, the amount of advertisement content in the stream is vastly inferior to the amount of programming content. If we focus on the first highlighted region, around frames 200 k, we see that some advertisements were not detected. This occurred due to self-advertisement in which the channel logo is present on the advertisements. Naturally, this occurrence impacts significantly on the recall rate. Another aspect is illustrated on the second region, between frames 380 k and 510 k (a considerable portion of the stream) where program was classified as advertisement due to logo absorption. An example of results in a common stream is depicted in Figure 11, with only some isolated frame level errors and visible significant performance of the proposed method. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Channel** & **Prec** & **TPR** & **F1** & **Acc** \\ \hline RTP1a & 29.10\% & 95.65\% & 44.62\% & 83.30\% \\ RTP1b & 45.18\% & 97.31\% & 61.71\% & 81.75\% \\ RTP2 & 20.04\% & 54.92\% & 29.36\% & 82.19\% \\ SIC & 97.25\% & 87.46\% & 92.10\% & 95.97\% \\ TVI & 82.50\% & 98.86\% & 89.94\% & 94.47\% \\ CMTV & 46.57\% & 99.99\% & 63.54\% & 83.66\% \\ Eurosport & 58.57\% & 100.00\% & 73.87\% & 86.38\% \\ Fox HD & 99.12\% & 100.00\% & 99.56\% & 99.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results obtained for the annotated dataset, analyzing the advertisement detections. Figure 11: Comparison of the results obtained for the SIC stream with the annotated data, illustrated as black bars the classification errors. Figure 10: Comparison of the results obtained for the RTP2 stream with the annotated data, illustrated as black bars the classification errors. Figure 9: Example of frames from the RTP2 stream where the detector failed to find the logo. In the left frame (**a**) the logo is imperceptible, even for humans.